A SOCIAL SEMANTIC WEB SYSTEM FOR
COORDINATING COMMUNICATION IN THE
ARCHITECTURE, ENGINEERING & CONSTRUCTION INDUSTRY
by
Jinyue Zhang
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Civil Engineering University of Toronto
© Copyright by Jinyue Zhang (2010)
ABSTRACT
Title: A Social Semantic Web System for Coordinating Communication in the Architecture, Engineering and Construction Industry Name: Jinyue Zhang Degree: Doctor of Philosophy Department: Department of Civil Engineering, University of Toronto Year of Convocation: 2010
The AEC industry has long been in need of effective modes of information exchange and knowledge sharing, but their practice in the industry is still far from satisfactory. In order to maintain their competence in a highly competitive environment and a globalized market, many organizations in the AEC industry have aimed at a move towards the development of learning organizations. Knowledge management has been seen as an effective way to have every member of an organization engaged in learning at all levels. At the very centre of knowledge management and learning is knowledge sharing through effective communication. Unfortunately, however, there is a big gap in the AEC industry between existing practice and the ideal in this area.
In order to effectively coordinate information and knowledge flow in the AEC industry, this present research has developed a framework for an information system – a Construction Information and Knowledge Protocol/Portal (CIKP) which integrates within it a publish/subscribe system, Semantic Web technology, and Social Web concepts. Publish/subscribe is an appropriate many-to-many, people-to-people communication paradigm for handling a highly fragmented industry such as construction. In order to enrich the expressiveness of publications and subscriptions, Semantic Web technology has been incorporated into this system through the development of ontologies as a formal and
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interoperable form of knowledge representation. This research first involved the development of a domain-level ontology (AR-Onto) to encapsulate knowledge about actors, roles, and their attributes in the AEC industry. AR-Onto was then extended and tailored to create an application-level ontology (CIKP-Onto) which has been used to support the semantics in the CIKP framework. Social Web concepts have been introduced to enrich the description of publications and subscriptions. Our aim has been to break down linear communication through social involvement and encourage a culture of sharing, and in the end, the CIKP framework has been developed to specify desired services in communicating information and knowledge, applicable technical approaches, and more importantly, the functions required to satisfy the needs of a variety of service scenarios.
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ACKNOWLEDGEMENTS
It is true that pursuing Ph.D. studies is a challenging undertaking, and it has been a life-changing experience for me. This thesis would not have been possible without the support I have received from many people in various ways. I would like to convey to them my heartfelt gratitude and sincere appreciation.
First and foremost, it would be hard to overstate my gratitude to my Ph.D. supervisor, Professor Tamer El-Diraby, for his continuous support and thoughtful guidance. With his wisdom, efforts and enthusiasm, many obstacles have been overcome smoothly. I also wish to thank Professor Hans-Arno Jacobsen and Professor Baher Abdulhai, my supervisory committee members, Professor Giovanni Grasselli, my additional examination committee member, and Professor Chimay Anumba, my external appraiser and examiner. I am indebted to all of them for their expert advice and constructive comments.
I would never have endured these many years without emotional support from my family. I will be forever thankful to my parents who have provided me with their deepest love and support throughout my lifetime and the course of my doctoral studies. I would also like to express my warmest thanks to my wife, Jiangyan, for her companionship and understanding throughout the long journey that I have undertaken. Finally, I must mention our lovely daughter, Annan, who has been my greatest joy throughout many hard-working days.
I am also very grateful to all the professors in the CEM Group and all my colleagues at the i2c Lab for sharing my struggle and making it possible for me to finish the program. I must thank the many domain experts who have provided valuable input and comments in the course of evaluation interviews and focus group study. Last, but not least, I would thank Patricia Bishop for her professional proofreading work.
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TABLE OF CONTENT
ABSTRACT ...... ii ACKNOWLEDGEMENTS...... iv TABLE OF CONTENT ...... v LIST OF TABLES...... ix LIST OF FIGURES ...... x
1 INTRODUCTION...... 1 1.1 Research Motivation...... 4 1.2 Problem Statement: A Micro Assessment of Coomunicaiton in AEC ...... 5 1.3 Proposed Solutions...... 9 1.3.1 An Publish/Subscribe System to Accommodate Industry Specialties ...... 9 1.3.2 Ontologies to Empower the Semantics...... 10 1.3.3 Social Involvement to Harness Collective Intelligence...... 11 1.4 Research Objectives...... 12 1.5 Scope, Contributions and Limitations...... 15 1.6 Thesis Organization ...... 18
2 LITERATURE REVIEW...... 20 2.1 Knowledge Management and Learning Organizations...... 24 2.1.1 Knowledge Management Practices in the AEC Industry...... 25 2.1.2 Barriers and Limitations to Knowledge Management ...... 26 2.2 Communication in the AEC Industry...... 27 2.2.1 Social Interaction of Communication in Construction ...... 28 2.2.2 Technical Communication in Construction...... 29 2.3 Publish/Subscribe Systems ...... 31 2.3.1 Types of Publish/Subscribe Systems ...... 32 2.3.2 Advantages...... 33 2.3.3 Limitations ...... 34 2.3.4 Potential Improvement...... 37 2.4 Web 2.0 and Social Web ...... 38 2.4.1 Weblogging (Blogging)...... 38 2.4.2 Wiki...... 39 2.4.3 Social Networking ...... 40 2.4.4 Social Tagging...... 41 2.4.5 Social Involvement in Information Systems...... 41 2.5 Semantic Web and Ontology...... 42 2.5.1 Ontology as Knowledge Representation...... 42
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2.5.2 Ontology Types...... 44 2.5.3 Ontology and Publish/Subscribe Systems ...... 45 2.6 Role Representation...... 45 2.6.1 Roles in Social Settings ...... 46 2.6.2 Approaches to Modelling Roles...... 47 2.6.3 Role Representation in the AEC Industry...... 48
3 RESEARCH METHODOLOGY...... 50 3.1 Methodology for the Development of the AR-Onto...... 52 3.1.1 Definition of Application Scenarios and Scope...... 52 3.1.2 Development of an Ontological Model...... 54 3.1.3 Development of Competency Questions ...... 55 3.1.4 Ontology Building...... 55 3.1.5 Ontology Coding...... 58 3.1.6 Ontology Evaluation and Documentation...... 61 3.2 Methodology for the Development of the CIKP-Onto ...... 63 3.3 Methodology for Developing the CIKP Framework ...... 66 3.3.1 Requirement Analysis...... 67 3.3.2 Social Involvement Definition...... 68 3.3.3 Basic Functions Definition ...... 69 3.3.4 Framework Evaluation...... 69
4 DOMAIN ONTOLOGY FOR ACTORS AND ROLES ...... 71 4.1 Requirement Analysis...... 72 4.2 Competency Questions...... 74 4.2.1 Hyponymy and Partonymy Questions ...... 74 4.2.2 Multi-view (Modality) Questions ...... 75 4.2.3 Attribute Questions...... 77 4.2.4 Cross-Tree Relationship Questions...... 78 4.2.5 Axiom Questions ...... 78 4.3 Main Ontological Model...... 79 4.4 Modelling Actors and Roles...... 83 4.4.1 The Actor-Role Debate...... 83 4.4.2 The Actor-Role Model...... 84 4.4.3 The Actor-Role Semantics ...... 87 4.4.4 The Actor-Role Mapping...... 90 4.5 AR-Onto Taxonomical Structure ...... 91 4.5.1 Taxonomy for Actors...... 92 4.5.2 Taxonomy for Roles...... 100 4.5.3 Taxonomy for Attributes...... 101 4.6 AR-Onto Relationships...... 105
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4.6.1 Major Types of Relationships ...... 105 4.6.2 Schema of Relationships...... 110 4.7 AR-Onto Axioms ...... 111 4.8 AR-Onto Modalities...... 112 4.9 AR-Onto Coding...... 116
5 APPLICATION ONTOLOGY FOR CIKP...... 119 5.1 Mechanism of Information and Knowledge Communication ...... 120 5.2 Requirement Analysis and Competency Questions for CIKP-Onto...... 125 5.2.1 Concept Questions...... 127 5.2.2 Relationship Questions...... 128 5.2.3 Axiom Questions ...... 129 5.3 CIKP-Onto Taxonomies...... 130 5.3.1 Entity Taxonomy...... 130 5.3.2 Entity Attribute Taxonomy...... 134 5.3.3 Value Pattern Taxonomy ...... 136 5.4 CIKP-Onto Relationships...... 137 5.4.1 Attributive Relationships...... 138 5.4.2 Other Cross-Tree Relationships ...... 141 5.5 CIKP-Onto Axioms...... 142 5.5.1 Restrictions ...... 142 5.5.2 Rules ...... 143 5.6 CIKP-Onto Coding...... 145
6 CIKP FRAMEWORK ...... 146 6.1 Requirement Analysis...... 148 6.1.1 Identification of Intended Users...... 149 6.1.2 Services Goal and Scenarios...... 150 6.1.3 Requirements of Technical Approaches...... 151 6.2 Social Involvement Methods...... 153 6.2.1 Social Interaction Methods Included in the CIKP Framework...... 153 6.2.2 Social Interaction Methods not Included in the CIKP Framework...... 155 6.3 System Architecture of CIKP Prototype ...... 155 6.4 Matching Algorithms ...... 158 6.5 CIKP Functions...... 155 6.5.1 Define and Edit User Profiles ...... 162 6.5.2 Publish Information or Knowledge...... 166 6.5.3 Subscribe to Published Items ...... 168 6.5.4 Browse Knowledge Items...... 170 6.5.5 Browse User Profiles ...... 173 6.5.6 User Home Page ...... 176
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6.5.7 Query the System...... 179 6.6 CIKP Framework vs. Existing Systems for Informaiton Management ...... 180
7 EVAL UATI O N ...... 182 7.1 Evaluation of the Ontologies ...... 182 7.1.1 Competency Question Review...... 184 7.1.2 Automatic Consistency Check...... 185 7.1.3 Expert Evaluation Interviews...... 188 7.2 Evaluation of the CIKP Framework...... 196 7.2.1 Requirement Conformity Assessment...... 198 7.2.2 Automatic Reasoning...... 198 7.2.3 Focus Group Study...... 200
8 CONCLUSIONS AND RECOMMENDATIONS ...... 208 8.1 Proposed Solution...... 210 8.2 Research Outcomes...... 211 8.3 Recommendations for Future Work...... 213 8.3.1 Recommendations Related to Ontology Development...... 213 8.3.2 Recommendations Related to the CIKP System...... 215
REFERENCES...... 218
APPENDIX A...... 235 APPENDIX B...... 240 APPENDIX C...... 244 APPENDIX D...... 247 APPENDIX E ...... 253 APPENDIX F ...... 264 APPENDIX G...... 276 APPENDIX H...... 283 APPENDIX I ...... 288 APPENDIX J...... 293
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LIST OF TABLES
Table 1-1: Research Scope...... 16 Table 2-1: Literatures Reviewed in Major Topics...... 21 Table 4-1: Competency Questions for Actors and Roles ...... 75 Table 4-2: Competency Questions for Modalities ...... 76 Table 4-3: Competency Questions for Attributes...... 77 Table 4-4: Competency Questions for Attributes...... 78 Table 4-5: Competency Questions for Axioms...... 79 Table 5-1: Competency Questions for Concepts in the CIKP-Onto ...... 127 Table 5-2: Competency Questions for Relationships in the CIKP-Onto ...... 128 Table 5-3: Competency Questions for Axioms in the CIKP-Onto...... 129 Table 7-1: Ontology Evaluation Criteria and Methods...... 184 Table 7-2: Respondents of Ontology Evaluation ...... 190 Table 7-3: Data Analysis of Question 1.4-1.7...... 193 Table 7-4: Data Analysis of Question 2.1 ...... 194 Table 7-5: Data Analysis of Question 2.2 ...... 194 Table 7-6: Data Analysis of Question 3.1 ...... 195 Table 7-7: Data Analysis of Question 4.1-4.4...... 196 Table 7-8: CIKP Framework Evaluation Criteria and Methods...... 198 Table 7-9: Data Analysis of Question 1.4-1.6...... 203 Table 7-10: Data Analysis of Question 3.1-3.7...... 204 Table 7-11: Data Analysis of Question 4.1-4.5...... 205 Table C-1: Major PCN Products ...... 245 Table C-2: Major PIP Products ...... 246 Table C-3: Major PPE Products...... 246 Table D-1: Comparison of Major Web 2.0 Services ...... 251
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LIST OF FIGURES
Figure 2-1: Barriers to KM Implementation by Level of Importance ...... 26 Figure 3-1: Research Methodology Overview...... 51 Figure 3-2: Methodology for the Development of the AR-Onto ...... 52 Figure 3-3: Sample of Axioms...... 61 Figure 3-4: Methodology for Proposing the CIKP Framework...... 67 Figure 4-1: Ontological Model of Construction Knowledge...... 80 Figure 4-2: Ontological Model of Entities...... 81 Figure 4-3: Separating Actors and Roles ...... 89 Figure 4-4: Actor-Role Assignment ...... 91 Figure 4-5: Outline of Actor Semantic Profile...... 91 Figure 4-6: Actor’s Taxonomy...... 97 Figure 4-7: Government Organization’s Taxonomy ...... 99 Figure 4-8: Major Contexts of Roles ...... 100 Figure 4-9: Attributes of Individual Actors...... 104 Figure 4-10: Attributes of Organizational Actors ...... 105 Figure 4-11: RNA’s Generic Relationships...... 106 Figure 4-12: Taxonomy Implementation ...... 116 Figure 4-13: Relationship Implementation ...... 117 Figure 4-14: Axiom Implementation ...... 118 Figure 5-1: Information and Knowledge Communication Model ...... 122 Figure 5-2: Example Object Attributive Relationships...... 139 Figure 5-3: Example Data Type Attributive Relationships...... 140 Figure 5-4: Partial View of Cross-Tree Relationships ...... 141 Figure 6-1: System Architecture of CIKP Prototype ...... 177 Figure 6-2: JTangPS Core Model...... 179
Figure 6-3: An Example of Assigning Values to Ric and Rir ...... 179 Figure 6-4: User Profile Definition (Blank)...... 163 Figure 6-5: User Profile Definition (Populated) ...... 164 Figure 6-6: Publish a Knowledge Item (Blank)...... 166 Figure 6-7: Publish a Knowledge Item (Populated)...... 167 Figure 6-8: Define Subscription (Blank) ...... 169 Figure 6-9: Define Subscription (Populated)...... 170 Figure 6-10: Browse Knowledge Items ...... 171 Figure 6-11: Browse User Profiles...... 174 Figure 6-12: User Home Page...... 177 Figure 6-13: Query the System ...... 179 Figure 7-1: Counterevidence of Ontology Consistency...... 188
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Figure 8-1: Problems, Goal, and Solution ...... 209 Figure B-1: General Communication System...... 241 Figure B-2: Message Delivery Mechanisms...... 242 Figure D-1: Web 1.0 Structure ...... 247 Figure D-2: Web Application Structure ...... 248 Figure D-3: Web 2.0 (Social Web) Structure ...... 249 Figure E-1: Semantic Web Structure...... 254 Figure E-2: Semantic Web Layers ...... 255 Figure J-1: SWRL Rule Example ...... 293 Figure J-2: Instance of Electrical Engineer...... 296 Figure J-3: Instance of Change Order Notice ...... 297 Figure J-4: Instance of Electrical and Wiring Contracting Company (Before Reasoning) .. 298 Figure J-5: Instance of Electrical and Wiring Contracting Company (After Reasoning)..... 299 Figure J-6: SWRL Rule and Jess Reasoning Engine ...... 300
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1 INTRODUCTION 1
he AEC (Architecture/Engineering/Construction) industry refers to a cluster that Tprimarily includes two economic sectors: professional services in architecture, engineering and other related fields and the construction industry. The complex and diverse nature of this industry demands effective and human-friendly communication systems because enhancing communication between project stakeholders is the first step in assuring efficient decision making in collaborative environments. The provision of suitable means of communication has been recognized by many modern enterprises as their primary requirement in assuring a ubiquitous flow of information and supporting the generation of collective intelligence by orchestrating the flow of knowledge and expertise amongst humans in different decision modes. More than advanced hardware systems, state-of-the-art work in communication systems focuses on the softer issues that concern the most effective way of communicating and the content to be communicated. These should include, for example, the way to deliver the right content to the right person at the right time, the method used for filtering the relevant information from a huge flow of information, or the approach to integrating the communication part into the decision making cycle.
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Despite the high level of demand in communication, the AEC industry has been characterized by a very low level of utilization of advanced communication systems. The reasons for such poor adaptation of knowledge and communication management protocols relate mainly to the fragmentation of the industry, its focus on short-term solution, and dynamic project environments. It is true that every individual project is distinct in terms of its location, style, functions, design team, and contractors, to name just some of its various aspects. In addition, there are many technical alternatives in each activity that are carried out over the entire lifespan of the project, regardless of whether the activity is big (for example, the selection of a project delivery method) or small (for example, the method of erecting a steel column.) Moreover, it is true that the information being communicated tend to be subjective and ad hoc. However, it is not true that we have to rely on the traditional communication approaches (oral or face-to-face communication with paper-based drawings and specifications) that have been the utilized for centuries in the AEC industry.
Most (if not all) construction projects share a similar life cycle and similar technical and organizational problems. Through the utilization of state-of-the-art communication systems we can strike a balance that capitalizes on the commonalities in the AEC domain and, at the same time, addresses the dynamic and human needs of individual projects/situations.
The solution proposed by this research attempts to do just that. It utilizes three communication technologies to create a consistent (yet dynamic) portal for project information exchange and domain knowledge sharing. First, an ontology (the main contribution of this research) has been developed to model the actors and their roles and associated information/communication needs. This ontology serves two purposes. First, it encapsulates the knowledge in the domain of communication in the AEC industry and provides for semantic annotation/search for documents or information artifacts. Second, a Publish/Subscribe (Pub/Sub) communication system has been deployed to match the
Chapter 1: Introduction 3 semantic profile of users (based on their roles and attributes as defined in the ontology) to the published information. In addition to its reliability and scalability, Pub/Sub systems provide for both pull and push communication modes. (You can search for what you want, or set the system to do just that for you every time a new and relevant document/piece of information comes through.) Relevancy is supported by the semantic matching of both actor and document/information. This matching also helps in filtering unwanted or irrelevant information. The final component in the proposed portal is inspired by Social Web concepts. Users can tag documents to enrich their semantic representation and/or vote on the tags/comments placed by other users to enrich their description.
The anticipated advantage of combining these three tools is better communication (delivering the right information to the right party at the right time.) With the merit of social semantic system, effectively exchanging project information could enhance the decision making process, and broadly sharing domain knowledge will have positive impacts on knowledge harnessing and learning. Professional services companies in the architectural and engineering fields are highly dependent on the knowledge of their employees to establish values, both for the clients and the companies, and thus they have tremendous interests in those organizational strategies. Construction companies usually lag behind in most innovations, but they have changed in recent years because of the low profits and highly competitive environment imposed by the trend toward globalization and the relatively low threshold for entering the market. A survey (Egbu, 2002) shows that the majority of organizations in the AEC industry agreed that knowledge management would result in new technologies and new processes that would benefit their organizations. Another survey (Carrillo et al., 2003) indicates that more than 40% of construction companies already had knowledge management strategies in 2003, and another 40% planned to have such strategies in place within a year.
Chapter 1: Introduction 4
1.1 RESEARCH MOTIVATION
The basic motivating scenario of this research envisions that various individuals and organizations will coordinate actions with each other by effectively exchanging project information and knowledge. Each industry participant (an individual or an organization) is treated as an actor and will be informed by the information and knowledge that they really need at the time they need it, and which is based on the profile of that actor and the role(s) that the actor is playing. The industry participants can also actively look up desired project information and knowledge in a flexible way. The effective communication on project information and knowledge is the foundation of effective decision making.
Another motivating scenario relates to domain-specific knowledge sharing. Project knowledge is useful in executing projects, but it is even more important is to distill, represent and reuse the domain knowledge gained during projects. The experience and lessons learned from each project, whether successful or not, should be kept and shared among the people who need them. Knowledge sharing will help the industry to avoid mistakes made during previous projects and prevent people from having to constantly reinvent the wheel.
The last motivating scenario is to support knowledge learning and sharing through the creation of communities of practice (CoPs) or virtual teams. For a variety of reasons, people in industry have an interest in information and knowledge not only about projects and science, but also about other people in industry (their peers.) With the trend toward globalization and more affordable and efficient means of telecommunication, industry participants have already broken through geographical boundaries to form more effective working teams. On the other hand, sharing experience in common areas of interest with globally allocated experts will elevate knowledge management practice to its highest possible level.
Chapter 1: Introduction 5
1.2 PROBLEM STATEMENT: A MACRO ASSESSMENT OF COMMUNICATION IN AEC
There are three major obstacles in the AEC industry that impede effective information exchange and knowledge sharing: fragmented and dynamic industry nature, lack of interoperable knowledge representation, and poor communication culture. This section will discuss those obstacles from different perspectives.
A Fragmented and Dynamic Industry The AEC industry is often seen as a fragmented industry. It is reported by Alberta Employment, Immigration, and Industry (2008) that 80% of Canadian construction companies have less than 100 employees and 45% employ less than 20 people. Another report by the Australian Taxation Office (2003) indicates that 90% of Australian construction companies are small companies. The same report shows that 99% of the companies in the construction industry are private. CNET News (Luening, 2000) reported in 2000 that, in the construction industry, the top eight companies in terms of market share control less than 20% of the industry, and this, in turn allows more companies to have a shot at the market. In the aerospace market, by contrast, the top companies control over 75% of all trade within the industry.
The first impact of fragmentation on knowledge management is the heterogeneous knowledge backgrounds of the industry’s workforce. Any single construction project will have to gather together many individuals and organizations to work on the project, and a uniform educational base or cognitive framework is never an assumption. This makes it difficult to share lessons learned or best practices. The second influence is the lack of common ground in interorganizational knowledge management. The large number of small businesses makes the market highly competitive, and thus most businesses tend to compete with one another by withholding their knowledge, and viewing it as a proprietary asset rather than sharing it. The third would be a lack of resources for knowledge management practices.
Chapter 1: Introduction 6
Small businesses are normally short on the manpower and budget needed to invest in projects such as knowledge management which will not show returns in a way that is short and direct. The highly competitive market keeps them busy shifting from one project to another, and they have no time to systematically document their experiences. Consequently, the industry is very focused on short-term actions and does not invest in long-term initiatives such as knowledge management systems. In short, the fragmentation of the AEC industry imposes a lot of difficulties on the strategic management of domain knowledge.
The dynamics of the AEC industry come from its project-based nature. Almost every business in the AEC industry is a project-based organization. Each project has a unique coalition of individuals performing tasks. A given individual or organization could be involved in more than one project at the same time, and play different roles simultaneously. For example, at any one given time, an architect could be a designer on one project, a project manager on another, and even an art consultant on a third. In addition, a project coalition tends to change dynamically during the lifespan of the project. As an example, the designer and contractor will leave the coalition after the physical construction is done and the facility manager will join the coalition at the same time, while the owner will remain in place throughout the lifespan of the project but with different focuses. Furthermore, an individual could change companies at any time during the project.
The different roles of an individual or organization in different contexts lead to different knowledge requirements and information needs; the same player may have different knowledge interests at the various stages of the project’s life cycle. For example, the information and knowledge an owner needs in the project-planning stage definitely differs from those needed at the operation stage. The experience and knowledge obtained by an employee should be as explicitly documented as possible before the employee leaves the organization in order to benefit similar projects in the future.
Chapter 1: Introduction 7
Lack of Formal and Interoperable Knowledge Representation “Knowledge Representation” (KR) refers to representations intended for processing by modern computers, and in particular, for representations consisting of explicit objects, and of assertions or claims about them. Representing knowledge in such an explicit form enables computers to draw conclusions from knowledge already stored. Interoperability is the ability of various system components to work together to accomplish a common task. The need for interoperable knowledge representation is much stronger in the AEC industry, because this industry involves people from a great variety of backgrounds, ranging from professionals in different fields to tradespeople who may be knowledgeable managers or on-site labourers.
There are two kinds of knowledge: tacit knowledge and explicit knowledge. Tacit knowledge is personal, context-specific, and is difficult to formalize and explain. This includes know-hows, crafts, skills, etc. Explicit knowledge is codified knowledge and refers to knowledge that is transmittable in formal systematic language (Polanyi, 1966). Most existing information systems focus on document management and knowledge classification, which means they handle only explicit knowledge. Tacit knowledge needs to first be explicitly distilled from the heads of experts, and then structurally documented before being processed by information systems. This definitely requires having a commonly agreed-upon knowledge representation system.
Due to the lack of interoperable knowledge representation, it is significantly harder for different systems to work together. Thus, many information systems used in the AEC industry for coordinating information and knowledge (for example, MS-Project, Primavera Project Planner, eConstruction, Edgebuilder, and many others) are able to process explicate knowledge inside the individual system, but find it hard to directly communicate a piece of information or knowledge generated by one system with the users of other systems.
Chapter 1: Introduction 8
Lastly, along with the astronomical expansion of available information and knowledge, whether it is in local libraries or online, it is, in fact, a harder job for average users to shop around for relevant information and knowledge, largely because of the tremendous amount of available resources and greatly varied modes of expression. Any information system is a place where the information and knowledge demand meets the supply.
The above-mentioned limitations cover three key components of the knowledge-management process: information and knowledge creation (explicitly expressing tacit knowledge), dissemination (communicating information and knowledge), and retrieval (finding relevant information and knowledge.) Therefore, an interoperable mode of knowledge expression is of crucial importance in this regard.
Poor Mechanism of Information and Knowledge Sharing Information and knowledge sharing is central to the success of all knowledge management strategies. A review of the existing information and knowledge communication practices in the AEC industry has revealed few issues: little attention is paid to interorganizational sharing, linear communication models are humdrum, and a knowledge-sharing culture is almost completely lacking.
A simple linear communication model means that a message is sent by an information source (a person) via a communication channel to a destination (another person). This model is still popular in many circumstances because people are used to following a pre-defined channel to send and/or acquire information. It is not realistic to expect to get all necessary communication channels pre-defined in the AEC industry due to its dynamic nature. With the one-way flow of a message from the sender to the receiver, the sender does not benefit from the knowledge-sharing process. The industry needs to foster a culture where knowledge sharing is a win-win process.
Chapter 1: Introduction 9
1.3 PROPOSED SOLUTIONS
This research proposes a Construction Information and Knowledge Protocol/Portal (CIKP) utilizing Semantic Web technology and Social Web concepts to coordinate information and knowledge flow in a publish/subscribe system for the AEC industry. The CIKP is a publish/subscribe system where knowledge publishers and knowledge subscribers are loosely coupled, and the matching between publications and subscriptions is more intelligent because of the involvement of Semantic Web technology and Social Web concepts.
1.3.1 An Publish/Subscribe System to Accommodate Industry Specialties
Publish/subscribe is a common communication pattern, and was adopted by the information and communication science in the late 1980s to handle large amounts of loosely coupled information producers and information consumers. Properly using this communication pattern could provide solutions to many knowledge management issues that are especially associated with the AEC industry.
Publish/subscribe is an appropriate approach to handling a very fragmented industry that is dominated by small businesses. Firstly, many companies have no resources with which to develop strategies/tools and train their employees to get used to managing knowledge. Rather, they depend highly on the knowledge of their employees. A publish/subscribe system can be deployed at any level (intraorganizational level, project coalition level, trade/profession level, or even the entire industry level), and no participating entity of this information and knowledge system needs to specially develop or invest anything, but only be willing to take part in it. Secondly, publish/subscribe systems do not require any participating entity to be coupled with another in either time or space. This feature fits the fragmented AEC industry very well because it nearly impossible to synchronize time and space among industry participants just for knowledge management purposes due to their highly mobile and
Chapter 1: Introduction 10 competitive natures. Thirdly, there is less fear of sharing knowledge with competitors. The nature of a publish/subscribe system allows for the sharing of information and knowledge with the whole participating community but not with any one particular entity. Instead, each participating entity shares with the whole and learns/benefits from the whole. Lastly, there is no (or less) information and knowledge loss after an individual leaves an organization. The individual leaving may have already shared his/her experiences and knowledge by publishing them, or ideally he/she will do so while working with another organization, and thus, that knowledge is still available to the industry.
The dynamic aspect of the AEC industry requires that publish/subscribe systems be intelligent in handling information demand and supply. A well-designed system needs to be able to dynamically represent a variety of roles that an industry participant could play and understand their respective needs for information and knowledge. More importantly, a mart system should be able to understand the semantics (real meanings) of information and knowledge to be handled.
1.3.2 Ontologies to Empower the Semantics
Based on interoperable knowledge representations, semantic systems offer an effective means of addressing distributed, subjective, and interlinked knowledge at both tacit and explicit levels. Ontologies have been seen as an appropriate carrier of interoperable knowledge representations to encapsulate knowledge in subjects of interest. An ontology is a formal, explicit specification of a shared conceptualization (Studer et al., 1998) within a certain context. Ideally, a universal ontology will represent all knowledge in the AEC industry to facilitate every kind of knowledge management process. However, this kind of universal ontology has not been (and probably will not be) developed within the AEC industry because of its nearly borderless scope. However, it is realistic to have a series of
Chapter 1: Introduction 11 interoperable ontologies to represent knowledge in the different areas of the AEC industry and to support individual systems. This will provide the whole industry with a theoretical base from which to communicate real semantics among different systems
Given the existence of interoperable ontologies, many of the limitations caused by a lack of common knowledge representations can hopefully be addressed: - Ontologies provide industry participants with a solid ground (a common industry language and expression structure) with which to explicitly document tacit knowledge, although they may not share the same cognitive framework. - Interoperable ontologies make possible the communication of real meaning between heterogeneous information systems, so that industry people will be able to use their favourite application to exchange information and share knowledge. - Not only are ontologies helpful in explicitly expressing the information demand and information supply, but also they are reasonable to generate knowledge that is not explicitly asserted and thus better match the demand side with the supply side.
1.3.3 Social Involvement to Harness Collective Intelligence
It has been mentioned that the AEC industry needs to foster an industry-wide culture of information and knowledge sharing. There are two reasons why industry participants are not willing to share information and knowledge: first they do not think they will benefit from the sharing process, and second, businesses mainly aim at competition but not collaboration while looking at knowledge assets. In this research, social involvement is encouraged to harness the collective intelligence of the crowd.
Social Web (or Web 2.0) has become a popular concept in Web applications over the last few years and is now treated as a new way to use the Web. Social Web offers a communication
Chapter 1: Introduction 12 platform to all users where they can not only contribute to the community but also benefit from it by learning from other participants. Combining the Social Web concept and the publish/subscribe communication pattern, the AEC industry should encourage an information and knowledge sharing culture because: - Every entity participating in social publish/subscribe systems combines the role of publisher and the role of subscriber. No participants are information and knowledge producers only, or information and knowledge consumers only. Every system user will need to contribute to the community and will benefit from the community. - A properly deployed publish/subscribe system will not only decouple publishers and subscribers, but also decouple organizations. This will break down the borders of organizations and make their sharing a people-to-people communication activity, and thus turn knowledge management from an organizational strategy into an industry-wide practice. - This new kind of social involvement will promote linear one-to-one mode of information and knowledge communication to the status of a multidirectional many-to-many mode. - Each published information and knowledge item will be socially reviewed and commented upon by many other system users so that the published item will be enriched not only by its original publisher but also by the community. This approach to information sharing is called the harnessing of collective intelligence.
1.4 RESEARCH OBJECTIVES
The ultimate goal of knowledge management is to get the right information and knowledge to the right people at the right time. The fundamental challenge in all scenarios is the flow of information and knowledge, i.e. how a piece of information or knowledge can be directed to the appropriate parties in a timely manner, regardless of whether it is about a project, a
Chapter 1: Introduction 13 technical topic in the AEC domain, or a person.
The overall objective of this research is to develop a system for web-based communication that is semantically-rich, socially-savvy, and technically-efficient to support effective flow of information in the AEC industry. Specifically, reaching this objective has entailed three tasks: (1) the development of an domain-level ontology to model actors, their roles and associated attributes and an application-level ontology tailored from the domain-level ontology to sever the proposed CIKP system from embedded knowledge of communication needs and patterns, (2) the implementation of a Pub/Sub system that will provide a technically-efficient environment for information exchange and knowledge sharing, and (3) the integration of best practices from Social Web applications to enrich the social aspect of communication.
In order to let the system provide better and more relevant matching between information sources and receivers, we need to make the system understand (1) the contents and meaning of the information and knowledge being communicated and (2) human needs for information and knowledge of humans. Here, there is a need to embed semantics in the system through the use of an ontology. As such, the first step is to develop a domain-level ontology (AR-Onto) to encapsulate industry knowledge about construction actors and roles. There is a need to extend modelling efforts from construction product and process to the focus of actors and roles, an aspect which is generally underrecognized. Most modelling efforts in the AEC industry are focused on building product models, with special attention given to representing the architectural components of buildings (Eastman, 1999). By contrast to the static product models, a large number of actors dynamically collaborate and co-operate in this domain. They have various intrinsic and extrinsic attributes which determine their knowledge needs and direct the flow of information; they are the subject of information and knowledge sharing. This ontology will develop a shared model about actors and their roles in the AEC industry for sharing both tacit and explicit knowledge, by representing the majority of industry actors,
Chapter 1: Introduction 14 their roles, their intrinsic attributes (personal identity, project affiliation, skills, etc.), and extrinsic (or role-related) attributes (responsibilities, liabilities, information needs, risks, etc.) This Actor-Role ontology will be combined with construction Product ontology and Process ontology to form a fuller knowledge representation of the industry. This ontology includes three interconnected components: major concepts of actors, roles and attributes, the semantic relationship between concepts, and the domain-level axioms/rules that govern the concepts.
There is also a need to tailor and extend the domain-level ontology (AR-Onto) to create an application-level ontology (CIKP-Onto) for proposed CIKP framework. Domain-level ontologies are normally comprehensive and cover the majority of the concepts in a given subject domain, but in order to functionally support a specific application, they need to be refined and extended to an application level that is based on a detailed analysis of individual applications. This research aims at establishing a publish/subscribe system that will facilitate information exchange and knowledge sharing. Thus, many application-specific concepts, relationships, and axioms need to be added, including, for example, the knowledge about items to be shared, the profiles of important actors and roles and the rules of inferring/matching, etc.
In the development of CIKP, there are two tasks to perform: the implementation of a Pub/Sub system and the integration of Social Web practices. A Pub/Sub system is a promising communication approach in the AEC industry because its communication paradigm decouples parties involved in communication – no one needs to be coupled with any other party in term of time, space and synchronization in order to make communication happen. This feature fits the complex and dynamic nature of the AEC industry very well. Furthermore, a Pub/Sub system supports both pull and push communication mechanisms, i.e., mechanisms where information and knowledge is found either by a person searching for it (pull mode) or through a piece of software code that searches for and delivers such information and
Chapter 1: Introduction 15 knowledge (push mode.) This will enhance the connectivity and timeless delivery of information flows. It could also tie information flows to work processes and the immediate needs of the receiver. Finally, if we can capitalize on the Social Web revolution to link people together in addition to linking documents and fostering the creation of Communities of Practice and a two-way communication model, that would be a major step in the right direction. After all, the sharing of information and knowledge is a social phenomenon and not just a physiological one.
1.5 SCOPE, CONTRIBUTIONS AND LIMITATIONS
This research is not meant to solve all the problems in AEC communication and knowledge sharing, but its fundamental assumption is that if we provide the industry with an effective communication portal/protocol, this will enhance the efficiency of communication. This, in turn, will have positive impacts on decision making and knowledge exchange/harnessing.
In the realm of a knowledge organization, it is hard to distinguish between communication, decision-making processes and knowledge harnessing/discovery. However, it would be a tall order to attempt to adequately address all of these issues in this thesis. Therefore, the chief focus of this work is on the use of social semantic systems along with a Pub/Sub communication paradigm to enhance communication flows among AEC professionals where they relate to technical issues. It is assumed indirectly, and as indicated by many other studies, that this will have a positive impact on decision making and knowledge harnessing. Investigating or proving this is beyond the scope of this research. Table 1-1 indicates the scope of this research in these three topics as they pertain to technical or non-technical issues.
With the increasing involvement of the public in AEC projects (especially in urban
Chapter 1: Introduction 16 infrastructure systems), it is possible that the proposed portal could be used in facilitating communication with those members of the public who do not necessarily have adequate professional experience. This potential, however, is also beyond the scope of this study. Addressing the non-technical issues or serving non-professional staff is a matter that requires an extensive study of dynamic social systems, e-democracy and urban decision-making policies. Again, this lies beyond the scope of this particular study.
The present research develops a web portal to illustrate the integration of social semantic systems along with a Pub/Sub system in construction communication. This portal is meant to showcase the proposed environment. Before it is used in real life, however, many issues need to be addressed, such as, for example, the policies of use (including security and access). How can participation be encouraged? Who can vote? How can misinformation be handled? Such questions are the work domain of other researchers (in group dynamics and business systems, for example) and are out of scope of this study.
Table 1-1: Research Scope Professional Staff Society At Large Technical issues Non-technical Issues Communication of Information/Knowledge Exact scope of this work Possible; out of scope Decision making Indirectly; out of scope Possible; out of scope Knowledge harnessing/discovery Indirectly; out of scope Possible; out of scope
The contribution of this research is twofold. It provides: (1) a domain-level ontology to represent knowledge about actors and roles in the AEC industry and an application-level ontology to model the information and knowledge sharing desired at the industrial level, and (2) a framework for coordinating information and knowledge flow in the AEC industry by utilizing already existing Social Web practices in a Pub/Sub system to realize a dynamic web-based environment for information exchange and knowledge sharing. It is not within the scope of this study to develop or propose new changes to Pub/Sub systems, nor is it within the scope of this study to develop or propose new changes to Social Web technologies and
Chapter 1: Introduction 17 systems. Again, these are areas for other researchers to explore. This present research will simply customize and use these technologies along with semantic systems to achieve the desired objective.
The fundamental contribution of this research is the two ontologies developed to represent domain knowledge in the AEC industry. The domain-level ontology in this thesis is the first research in the AEC industry that will systematically model actors, roles and their attributes. This research extends the modelling efforts from construction products and business processes into the area of actors and roles. It is an ontology that could be extended to and utilized in many application areas that involve the study of industry actors and roles such as, for example, the study of business behaviours in process reengineering. The application ontology is the first model to represent the information and knowledge needs of major construction actors and roles. It deserves to be treated separately because it incorporates within it knowledge of technical communication. Its semantic relationships focus on information flow, and its axioms emphasize more about the information and knowledge needs of actors and roles.
The proposed CIKP framework is the first endeavour to blend the merits of Semantic Web technology, Social Web concepts and publish/subscribe systems. Some researchers have proposed and studied semantic publish/subscribe systems to realize content-based matching, while others have suggested coupling the Social Web and the Semantic Web to provide advanced and meaningful web services, but this is the first time that the three have been brought together as a social semantic system for information and knowledge management. Another distinct aspect of this research is that, for the first time, one information system is proposed that can fully handle four kinds of knowledge (known known, known unknown, unknown known, and unknown unknown) in cognitive science.
Chapter 1: Introduction 18
This research has some limitations in terms of its scope and additional work is required if fuller coverage is desired: - The domain coverage of the AR-Onto is the general AEC industry. For this reason, the concept coverage for the actors and roles is very comprehensive, however, the expressiveness of some individual concepts may need further extension. - The application-level ontology CIKP-Onto focuses on coordinating design and planning information and knowledge. There are simply too many kinds of information and knowledge for it to be possible to provide a single ontology to satisfy all purposes. Design and planning are more knowledge intensive than other life-cycle stages. Thus, we first aims at these two stages and other stages can easily be covered by extending it. - The CIKP framework focuses on requirement analysis and system functions, because they are the key issues associated with the AEC industry. There are many technical issues such as system topology, protocol design, etc. in the development of an information system, but they are not in the scope of this research. Some policy issues closely related to the Social Web concept are discussed in this framework. - Ontology is used to explicitly and structurally document tacit knowledge in this research, however, due to the scope of the ontology, only a part of tacit knowledge related to technical issues in the AEC industry can be documented as explicit knowledge. Although this study only covers technical issues, both technical and social perspectives of communication of technical issues among AEC professionals are respectively addressed through the use of Semantic Web technologies and Social Web concepts.
1.6 THESIS ORGANIZATION
This thesis is organized into eight chapters. This chapter introduces the motivation for the research and presents existing problems, proposes solutions, and outlines the corresponding
Chapter 1: Introduction 19 contributions of this research in managing information and knowledge in the AEC industry.
Chapter 2 is a literature review that introduces some major topics that are closely related to this research: knowledge management and learning organizations, communication issues in the AEC industry, publish/subscribe systems, the Social Web, ontologies and Semantic Web, and role representation. Chapter 3 presents the methodology adopted in this research. This chapter focuses on the methodology of building ontologies and requirement analysis of the CIKP framework.
Chapters 4 and 5 present the domain-level ontology (AR-Onto) and the application-level ontology (CIKP-Onto). Chapter 4 discusses competency questions, the ontological model, the philosophical and technical bases for differentiating actors and roles, ontology itself and its implementation. Chapter 5 mainly discusses the extension of domain-level ontology to cover information and knowledge sharing issues in an application-level ontology.
Chapter 6 presents the proposed framework of the CIKP system. This chapter focuses on requirement analysis and the definition of system functions. Some policy issues related to social involvement are addressed. It also compares the CIKP framework with existing information management software applications available to the AEC industry.
Chapter 7 presents the research evaluation process, its results and result analysis. The methods used to evaluate two ontologies include competency question reviews, automatic consistency checking, and expert interviews, while the methods used to evaluate the CIKP framework are requirement conformity assessment, automatic reasoning, and focus group study.
Chapter 8 summarizes the research and provides some thoughts for future research.
Chapter 1: Introduction
2 LITERATURE REVIEW 2
n order to achieve the objective proposed in Chapter 1, a set of literatures has been I carefully studied. Several topics have been investigated for their impact on this research and the potential advantages of their application. The topics are learning organizations, knowledge management, technical communication, publish/subscribe systems, Semantic Web and ontologies, Social Web (Web 2.0), and role representation. Table 2-1 indicates the major issues that have been reviewed for each topic and the references that are associated with the issues involved.
In order to maintain overall competence in a highly competitive environment, many organizations have planned or are planning to make the transition towards learning organizations through effective knowledge management. Due to the broad meaning of the two concepts (learning organization and knowledge management), it is commonly agreed by researchers that a set of principles is more important than a standard definition when studying their impact and application. A learning organization needs to foster a culture of learning
20 21 viewed in Major Topics Topics viewed in Major
Table 2-1: Literatures Re Table
Chapter 2: Literature Review 22 through a set of management processes assisted by proper tools and techniques. Knowledge management concerns the management of all aspects of knowledge (the knowledge itself, the learning process, and the learning system) throughout the entire life cycle of the knowledge involved. Currently, knowledge management has gained broad recognition in the AEC industry, and industrial organizations have begun to know the significance of becoming learning organizations. However, most industry applications of knowledge management emphasize the use of IT. In fact, however, IT is only a tool to enable knowledge management but is not its essence. Among the many aspects of knowledge management, most existing practices only focus on document management and knowledge classification. There are several noticeable obstacles to the effective implementation of the two organizational strategies: lack of standard work processes, time and money constraints, weak passion and culture, and poor IT infrastructure.
The importance of effective communication is well known in many industrial sectors including the AEC industry, yet the published literatures regarding the communication in the construction industry is scarce (Emmitt and Gorse, 2007). Social communication emphasizes the transfer of tacit knowledge through human interaction, while technical communication focuses on explicit knowledge transferred by a variety of information and knowledge systems. A small number of doctoral studies have tackled aspects of social interaction of communication in construction (Kreiner, 1976; Wallance, 1987; Gameson, 1992; Pietroforte, 1992; Bowen, 1993; Loosemore, 1996; Emmitt, 1997; Larsson, 1992; Hugill, 2001; Gorse, 2002; den Otter, 2005). Some other researchers have paid more attention to the technical part of communication in construction, especially the online ICT tools used to facilitate communication (Rowlinson and Croker, 2006; Lee and Bernold, 2008; Weippert et al., 2003; Cheng et al., 2001; Weippert et al., 2002; Adriaanse and Voordijk, 2005; Mohamed and Stewart, 2002; Abadi, 2005; Adriaanse et al., 2004). A better information system should be able to address these two aspects of communication through the proper selection of message
Chapter 2: Literature Review 23 delivery mechanisms and communication channels.
Our review of the literature has indicated that, among the many communication approaches and models, publish/subscribe is an appropriate approach to information exchange and knowledge sharing in a widely distributed and dynamic industry such as construction. Publish/subscribe systems are scalable and fully decouple the information producers and consumers in time, space, and synchronization. From a perspective of human and social interaction, they also promote many-to-many, people-to-people communication. Many researchers have studied possible improvement to publish/subscribe systems, and the semantic expression of events has been a popular topic for several years. The application of ontologies not only promotes the syntactic match to semantic match, but may also provide the systems with intelligence. The limitations related to this research are two assumptions assumed by many traditional publish/subscribe systems: (1) every published item is good and free to share, and (2) subscribers know with certainty what to subscribe to.
The Social Web and Semantic Web are two new trends that have emerged in last few years. The Social Web describes the new way of using the Web to enhance creativity, information sharing, and collaboration among users, while Semantic Web aims at the real meaning of online content for better interoperation. Some researchers such as Gruber (2006) have studied the integration of Social Web and Semantic Web to achieve better online services and others have studied the integration of either Social Web or Semantic Web with other technologies, for example, Petrovice et al. (2003) who investigated the application of ontology in publish/subscribe systems for a better matching algorithm. This research took a further step in integrating semantic technology in a publish/subscribe system by applying the reasoning capability of ontologies. In addition, this research also went so far as to incorporate Social Web concepts into a publish/subscribe system.
Chapter 2: Literature Review 24
One important issue in communication is the representation of the sender and receiver of information or knowledge. As the origin and destination of a flow of information or knowledge, the sender and receiver are industry actors who play different roles in the AEC industry. Understanding the attributes and functions of industry actors and their roles is essential to the design of communication systems. Significant research efforts have been dedicated to construction product modelling and construction process modelling. Little work has been done on modelling actors and roles in construction knowledge. With the evolution of the Social Web and Semantic Web, it is important to profile actors and roles to support the proper flow of information and exchange of knowledge. There has been a long debate on the issue of separating actors and roles in the domain of philosophy, and it has been concluded that actors and roles lie on a fuzzy continuum. Most literatures in the AEC industry do not separate actors and roles. A set of roles features has been reviewed and has been used to identify industry roles.
2.1 KNOWLEDGE MANAGEMENT AND LEARNING ORGANIZATIONS
Even though the phrase “Knowledge Management (KM)” only came into common use after the publication of Nonaka and Takeuchi’s (1995) text The Knowledge Creating Company, knowledge itself and the activities related to knowledge have been topics for discussion for centuries. At this point in human history, the basic rationale for knowledge management on the part of many organizations is the need for a transition towards their establishment as learning organizations. With the impact of rapid market change and a globalized workforce, only those organizations that are flexible, adaptive, and productive will excel. For this to happen, organizations need to develop a strategy to have their people engaged in learning at all levels. Increasingly, the concepts of Learning Organization (LO) and Knowledge Management are seen as two sides of the same coin. Learning happens in the process of managing knowledge, and the knowledge gained through learning can be applied to next
Chapter 2: Literature Review 25 learning cycle. Appendix A discusses the motivation for organizations to be learning organizations and the advantages of knowledge management in helping the transition towards this end. This section will review the current status of applying knowledge management in the AEC industry and criticize the industrial barriers and limitations of applying knowledge management.
2.1.1 Knowledge Management Practices in the AEC Industry
Construction projects are labour and knowledge intensive. Many professionals, acting as knowledge workers are involved in the planning, design, and project management activities of construction projects. These professionals use the knowledge they have gained in training, schooling, or from previous work experience. Field construction used to be labour intensive, but due to the increasing automation of construction processes and the recognition of management practices in the domain, more and more construction is becoming knowledge-dependent. The operation and maintenance of built facilities is also highly dependent on knowledgeable professionals and skilled operators. In short, knowledge is a necessity for the entire AEC industry, and thus there is a need to manage knowledge.
A study conducted by the Construction Industry Institute (CII) shows that the industry is pretty fragmented in terms of KM and LO (CII, 2006). Almost all the industrial organizations surveyed have claimed that they have a process for capturing lessons learned, but about half of them lack a regularly scheduled post-project review process – and this only happens after a problem has been found.
In the UK, many projects attempted to promote KM in the construction industry through “Benchmarking Knowledge Management Practice in Construction” (Dent and Montague, 2004) and charter the use of KM such as “Partners in Innovation Project’s Knowledge
Chapter 2: Literature Review 26
Management for Sustainable Construction Competitiveness” (McCrea, 2004). In addition, much research work has been initiated by universities or industry collaborators in the UK (Venters et al., 2002; KLICON, 1999; Graham and Thomas, 2005). In the industry, many leading engineering design and construction firms have appointed dedicated knowledge managers to oversee their knowledge management activities (Carrillo and Chinowsky, 2006).
2.1.2 Barriers and Limitations to Knowledge Management
Carrillo et al. (2004) ranked six major challenges faced by many organizations (Figure 2-1). Although the figures came from a survey of construction organizations, other sectors in the AEC industry were also shown to experience similar challenges (Kelleher and Levene, 2001).
Figure 2-1: Barriers to KM Implementation by Level of Importance (Carrillo et al., 2004)
It is commonly agreed that the lack of standard work processes for managing personal and organizational knowledge is a key missing factor. This is more obvious in small- or medium-sized organizations where they lack the human and financial resources to implement
Chapter 2: Literature Review 27 formal processes, and therefore, a great deal of knowledge management work is done “ad hoc” with no systematic planning, supervision, or evaluation activities. In addition, some implemented knowledge management processes will be in jeopardy if an organization merger or acquisition occurs. By contrast, large organizations are more stable and find it easier to implement formal and standardized processes to promote knowledge management. Some other barriers that require action from top management include lack of time, lack of money, organizational culture, and employee resistance, as well as the relatively low recognition of IT infrastructure.
Although the significance of knowledge management has been recognized for more than ten years by government, academia, and industry, a structural approach to managing knowledge derived from construction projects is still very far from perfection (Al-Ghassani et al., 2004): - Several industry surveys (Carrillo et al., 2004; Earl, 2001) have discovered that most KM practices focus on document management or knowledge classification (Lin et al., 2005). - Some research outcomes (Bair and O’Connor, 1998; Zack, 1999; Tiwana, 2000; Al-Ghassani et al., 2001) point out that IT tools are very useful in managing explicit knowledge, but they do not adequately address the issue of managing tacit knowledge. - One limitation not noticed by many researchers is that most KM practices only focus on one or just a few stages of a project’s life cycle and deal with a subset of project stakeholders. - The nature of organizational learning determines the scope of KM and is limited within individual organizations.
2.2 COMMUNICATION IN THE AEC INDUSTRY
Communication in the AEC industry simply refers to the transmission of meaningful
Chapter 2: Literature Review 28 information and/or knowledge from one party to another through the use of shared symbols and media. Effective and efficient communication is of vital importance in the AEC industry (Dawood et al., 2002). This importance can be seen at all three levels: the organizational level, the project (team) level, and the personal level. The current industrial practice of communication is far from satisfactory and it is commonly agreed that ICT is a powerful approach to solving the problem. However, the use of ICT in construction is still far lagging behind many other industries.
The effectiveness of communication in the AEC industry is a significant factor in successfully completing construction projects, because there are numerous individuals (engineers, technicians, facility operators, construction labourers, etc.) and organizations (design firms, government departments, financial institutions, law offices, etc.) involved in these projects. Communication between organizations and individuals temporarily brought together for a construction project is undertaken regularly, but has not received as much attention from researchers as have other aspects of construction such as productivity and safety. As a background, Appendix B reviews human social communication and technical communication in a general context. This section will review communication in the AEC industry and discuss ICT in construction communication.
2.2.1 Social Interaction of Communication in Construction
Gameson (1992) investigated communicative interaction during the feasibility stage by arranging meetings between potential building clients (experienced or inexperienced) and different types of construction professionals. The findings supported the conclusion that different types of professionals and clients exhibited different interaction patterns depending on their profession and experience. Construction professionals from the same profession tended to use some words from their field more than did other professionals. Gameson also
Chapter 2: Literature Review 29 found that the more experienced parties (either the clients or the professional consultants) tended to dominate communication during the meetings.
The interaction of major actors in the pre-contract phase (architect, client, consulting engineer, and quantity surveyor) was investigated by Wallace (1987). It was found that the decisions made by the architect were a function of an evolving group-development process. Wallace (1987) concluded that the less influential subjects in the group process were less assertive and made fewer attacks on proposals, while the more influential parties tended to ask questions to extract information and give direction.
Kreiner (1976) studied construction-site-progress meetings focusing on the social relationships on construction sites in Denmark. The finding was that the site meetings were more like “ceremonial events” because actors met to act out relationships in accordance with a fixed agenda, and some participants did not contribute to the debate at all. Loosemore (1996) observed the communication involved in resolving crises during the construction phase. The actors observed included clients, estates-department representatives, project managers, architects, clients’ quantity surveyors, contractors’ project managers, and contractors’ quantity surveyors. Loosemore found that communication networks emerged out of a need for information to overcome a crisis. The conflict due to construction crises normally discouraged collective responsibility and reduced the effectiveness of the communication network.
2.2.2 Technical Communication in Construction
The information or knowledge being communicated in the AEC industry includes both paper-based and electrical documents such as contact documents, drawings and specifications, communication correspondence, and so on. The majority of business processes in this
Chapter 2: Literature Review 30 industry are heavily based upon traditional means of communication such as face-to-face meetings and the exchange of paper-based documents in the form of technical drawings, specifications, and site instructions (Mohamed and Stewart, 2003). It has long been recognized by the industry that there is a need for an information system to facilitate the exchange of massive volumes of information and the sharing of tacit knowledge. As mentioned before, most of the research that has been done in the construction industry has focused on one or just a few of the components of technical communication. The needs of the information source and the receiver have been partially addressed by research into the social-interaction aspect of communication, but have not been fully covered in terms of the motivation of the information source and the needs of the information receiver. The encoder, decoder and channel have been discussed mostly in the domain of ICT applications (Finch, 2000; Cole, 2000). The study of the message being transferred is not popular and the most significant research is the expression of message semantics through the use of domain ontologies (El-Diraby, 2005).
Most past and existing research on technical communication in construction is about the ICT application, because the industry has suffered for many years from difficult-to-access, out-of-date and incomplete information (Shoesmith, 1995). The most popular studies are those of ICT applications for communicating on a single project or among organizations. A number of ICT tools that use network technology have been proposed to facilitate project communications. Nitithamyong and Skibniewski (2004) categorize all Web-based Project Management Systems (WPMSs) into three classes: Project Collaboration Network (PCN), Project Information Portal (PIP), and Project Procurement Exchange (PPE). Appendix C summarizes the major PCNs, PIPs, and PPEs that are currently available and that have been developed specially for the construction industry (Nitithamyong and Skibniewski, 2004). In order to attain a higher level of competitiveness, and serve clients better, some ASPs have started to combine two or three of the afore-mentioned service types in their business model.
Chapter 2: Literature Review 31
For example, Causeway Solutions (www.causeway-tech.com), CXTM Solutions (www.aconex.com), e-Manage NetTM (www.microlar.com), and PrimeContractTM (www.primecontract.com) have integrated all PCN, PIP, and PPE services in their servers. Although there are many off-the-shelf systems available in the market, the majority of them are just document management systems or platforms for exchanging data.
2.3 PUBLISH/SUBSCRIBE SYSTEMS
Communication takes place at two dimensions: technical communication to transfer explicit knowledge (and information) and social communication to transfer tacit knowledge. A human-savvy information system could be one means to address these two aspects of communication. One potential option in this regard is a system that is based on publish/subscribe mechanism.
Publish/subscribe is a loosely coupled communication paradigm for distrusted computing environments. In publish/subscribe applications, some participating systems or users (publishers) publish information (events) to a negotiating system (event broker), while some participating systems or users (subscribers) express their interests in published events through subscriptions. The responsibility of the event broker is to ensure the timely delivery of published events to all matched subscribers. The concept of publish/subscribe was first mentioned by Zwaenepoel and Cheriton (1985) in a paper on multicast in the V System. Frank Schmuck should be credited as the first person to implement a fully functional publish/subscribe solution in the “news subsystem” in the ISIS Toolkit which was described in a paper by Birman and Joseph (1987) in an ACM Symposium on Operating Systems Principles Conference in 1987. Recently many researchers are working on various aspects of publish/subscribe systems, for example, its overlay topologies (Parzyjegla et al., 2006; Jaeger,
Chapter 2: Literature Review 32
2005), event matching algorithms (Leung and Jacobsen, 2003; Muthusamy and Jacobsen, 2005), event semantics (Wang et al., 2004a; Petrovic et al., 2003), system scalability (Zhang et al., 2005), and system security (Wang et. al., 2002).
2.3.1 Types of Publish/Subscribe Systems
There are several ways to specify the interests of subscribers and this has led to different types of publish/subscribe systems. The two most widely-used publish/subscribe system types are topic-based and content-based.
The earliest publish/subscribe systems were topic-based systems, which depend on the notion of topics or subjects. In these systems, a set of topic spaces is predefined. Each event is labelled as one of a fixed set of topics. The providers are required to specify which topic that an event belongs to. When consumers subscribe to a particular topic, they receive all the events that are labelled with the topic. In the past decade, systems supporting this paradigm have matured significantly resulting in several solutions (Altherr et al., 1999; Corporation, 1999; Skeen, 1998; TIBCO, 1999). However, this kind of systems lacks the flexibility needed to customize subscriptions. Information consumers will receive all the events in a topic group labelled with a keyword that matches the keyword in the subscription, regardless of the content of those events.
Due to the limited expressiveness of topic-based event representation, content-based publish/subscribe systems were proposed (Aguilera et al., 1999). The improvement lies in the introduction of a subscription schema based on the actual content of the events involved. In other words, events are not classified according to some pre-defined external criterion (e.g., topic name), but according to the attributes of the events themselves (Eugster et al., 2003). Consumers subscribe to selected events by specifying filters using a subscription language.
Chapter 2: Literature Review 33
The filters define constraints, usually in the form of (attribute, value) pairs and basic comparison operators (=, <, >, <=, >=), which identify valid events. Constraints can be logically combined (and, or, not, etc.) to form complex subscription patterns.
2.3.2 Advantages
The major advantage of publish/subscribe systems is that publishers are loosely coupled with subscribers – they need not even know of one another’s existence. This characteristic makes publish/subscribe systems meet the requirements of the large-scale settings of infrastructure and construction. Subscribers can express their interest in an event or a pattern of events, and be subsequently notified of any event that is generated by a publisher and that matches the subscription (the registered event posted by subscribers). This matching event is asynchronously propagated to all subscribers who have this interest clearly registered in the system. This kind of decoupling in time, space, and synchronization between publishers and subscribers is the core strength of event-based publish/subscribe systems.
Another advantage of publish/subscribe systems is its scalability. In telecommunication and software engineering, scalability is a desirable property of a system, a network, or a process, that indicates its ability to either handle growing amounts of work in a graceful manner, or to be readily enlarged (Bondi, 2000). A system that maintains the designed quality of performance after being applied to a large load, for example, a large input data set or large number of users, is said to be a scalable system. Publish/subscribe systems provide the opportunity for better scalability than do traditional client-server systems through parallel operation, message caching, tree-based or network-based routing, etc.
The above-mentioned two advantages (decoupling and scalability) of publish/subscribe systems pertain primarily to the technical aspect of system development, and more
Chapter 2: Literature Review 34 specifically to that of computer science and software engineering. However, as has been discussed in previous sections, communication is a human activity and is largely influenced by social and cultural factors. From the perspective of the human dimension, publish/subscribe systems promote the concept of participation (instead of information search/collection) and a many-to-many communication pattern (instead of one-to-one or one-to-many communication).
Publish/subscribe systems provide the opportunity to combine the two roles (event producer and event consumer) to form a new entity which is called the “system participant”. This interactive model, which is different from the traditional “service provider – user” interaction approach, encourages a culture of sharing. A system participant carries a dual role in the interaction of communication: information producer (a publisher) and information consumer (a subscriber). In the traditional information-management model, there is only unidirectional information flow, which means a provider only produces information and a user only searches for information. Information providers do not benefit from the unidirectional activity, so they may lose interest in contributing to the system.
The Publish/subscribe communication paradigm is also a breakthrough from the linear one-to-one and multicast one-to-many communication models. It promotes a culture of many-to-many communication. The information flows are operated in a parallel fashion where one participant can send information to multiple destinations and receive information from multiple sources. Many-to-many communication is the fundamental requirement of an information system pursuing collective intelligence (see Section 2.4 for further details)
2.3.3 Limitations
Reliability is an important feature of distributed information systems, and is the biggest
Chapter 2: Literature Review 35 concern about publish/subscribe systems because subscribed events (messages carrying some wanted information) cannot be guaranteed on delivery in some situations. This is actually a side effect of the main advantage of publish/subscribe systems, i.e., the decoupling feature between publishers and subscribers. Systems that allow subscribers and publishers to communicate directly with each other, such as the systems deployed on a pure Peer-to-Peer network (with no super nodes), normally do not guarantee event delivery, because events are not kept in the system for failed or disconnected subscribers. It has been recommended that, in order to achieve reliable delivery, certain processes need to be established and specifically geared to storing events (Eugster et al., 2003).
The second problem in existing publish/subscribe systems is the way in which events are expressed. - Structural information of events is a major consideration for most existing systems in matching published events with subscriptions (Wang et al., 2004a). This is only the matching of syntax, but no sense of the semantics of events is supported in these systems. A system would be more intelligent in matching publications and subscriptions and in delivering “real information” if it could understand both syntax and semantics. - Most existing publish/subscribe systems lack flexibility in their expression of events. Information producers and consumers have to follow either a set of pre-defined topics or a pre-defined schema to describe the content, but they are not able to express events completely based on their own thoughts. - Then there is a dilemma between the expressiveness of events and the efficiency of matching algorithm. Normally the data structure of events determines the design of matching algorithm, and the more complex the data structure of events is, the more difficult it is to design an efficient matching algorithm.
Another potential problem for publish/subscribe systems is their Quality of Service (QoS).
Chapter 2: Literature Review 36
Again, this is related to the human dimension and not the technical aspect. There are two very naive assumptions about publish/subscribe systems: (1) All events published are good and free to share, and (2) Subscribers know with certainty what to subscribe to. This may look ridiculous, but it is indeed the way publish/subscribe systems are currently designed.
There have been a lot of research works focusing on the security aspect of publish/subscribe systems, but most of them (if not all) still hold to the assumption that all information is good and free to share. For example, Srivatsa and Liu (2005) introduced EventGuard for securing publish/subscribe overlay services, and the first assumption they laid on publishers is that “EventGuard assumes that authorized publishers are honest and publish only valid events”. Most research work on security is based on this assumption and pays more attention to the authentication of a publisher, the confidentiality and integrity of events being transferred, or Denial of Service (DoS). However, the fact is that not all the information published into the system is good and free to share. Someone could spam or flood the network by pushing a large amount of fake information into the system; someone could unintentionally publish wrong information, and someone could be looking for unauthorized information. There is a need for a mechanism to validate published events.
The nature of publish/subscribe systems leaves the decision about “what to subscribe to” to the subscribers, as it is the absolute power of subscribers to express their interest in published events. In reality, many people (or the designer of the applications that will be for the subscribers in an integrated architecture consisting of several systems) are not able to completely express their interests, although most of them can subscribe to the most important and obvious information items. This is particularly true for novices in a domain, yet it happens occasionally to domain professionals. The system would reach a higher QoS if it were able to partially infer some implicit subscriptions based on domain knowledge.
Chapter 2: Literature Review 37
2.3.4 Potential Improvement
Our review of the literature have indicated that, among the many communication approaches and models, many-to-many online communication via a distributed computing network is an appropriate approach to information exchange and knowledge sharing in a widely distributed and dynamic industry such as construction. From the point of view of ICT, publish/subscribe systems are scalable and fully decouple the information producers and consumers in time, space, and synchronization, while from the point of view of social and cultural activities, publish/subscribe systems provide opportunities for integrating the role of information provider and information consumer and allow for many-to-many communication.
The expression of events is one of the major concerns of most publish/subscribe systems. A lot of research has been done in the past few years with the purpose of embedding semantics in the matching algorithm to enrich expressiveness (Zeng and Lei, 2004; Petrovic, 2003; Wang et al., 2004a; Li and Jiang, 2004; Wang et al., 2004b; Ma et al., 2006). A lack of expressiveness on either the publication side or the subscription side will jeopardize the matching of events. Most recent research on semantic publish/subscribe systems employ taxonomies or ontologies to empower the semantics, such as the OPS system (Wang et al., 2004).
A well-defined ontology can not only enrich the expressiveness of events, but also has the ability to infer the information needs of each actor and role based on the knowledge encapsulated in the ontology. This could well address the problem of the wrongful assumption that “subscribers know with certainty what to subscribe to.” This research has proposed two kinds of subscriptions: explicit subscriptions (subscription events manually created by users) and implicit subscriptions (subscription events inferred by the ontology according to domain knowledge).
Chapter 2: Literature Review 38
This research hires the concept of Social Web to address the problem of “all information is good and free to share” in publish/subscribe systems. The Social Web service (see Section 2.4) provides the opportunity to amend or augment the description of a published event by the wisdom of crowds. Any subscriber who receives information about an event could have the power to amend any mistakes or errors and/or augment the description of the event by adding new tags to the event. This newly added description, after being approved/validated, is the intelligence arising from social participation and will make events (in turn the system) more expressive.
2.4 WEB 2.0 AND SOCIAL WEB
At the beginning of the new millennium, there was a noticeable shift in how people used the Web and how businesses developed Web-based applications. Tim O’Reilly named this new paradigm, using the term “Web 2.0” (O’Reilly, 2005). Most Web 2.0 applications support the social interaction of users, and therefore, it is also called the Social Web. As a general background, Appendix D addresses the evolution of the Web and Web 2.0.
Social Web applications can be roughly categorized into four types: Blog, Wiki, Social Networking, and Social Tagging. The common feature of these Web applications is that they use the Web as an interacting platform for creating collaborative and community-based services.
2.4.1 Weblogging (Blogging)
The word “Blog” is a short from of the word “Weblog”. The term Weblog was coined by Jorn Barger (Wortham, 2007) in 1997. A blog is basically a website with regular entries of
Chapter 2: Literature Review 39 commentary, descriptions of events, or even graphics or videos. The material of a blog is commonly displayed in reverse chronological order. A common metaphor for Blog is an online diary because most blogs are maintained by individuals to document their personal lives. Other blogs are created by either organizations or individuals to provide commentary or news on a particular subject. A typical blog displays a combination of various media formats including text, images, videos, and URLs.
2.4.2 Wiki
A wiki is a type of website that provides free online space to anyone who accesses the space to contribute to or modify the content published in the space. Wiki’s are often used to create collaborative websites. If something is missing or incorrect in the content of a wiki website, it can be easily amended by adding new thoughts or making changes, provided that the requisite permission to edit the content has been obtained.
WikiWikiWeb, the World’s first wiki website, was developed by Ward Cunningham in 1994 and implemented online in 1995 (Cunningham, 2008). The name Wiki was inspired by a Honolulu International Airport shuttle bus called Wiki-Wiki which means “very fast”. Three key features of wikis have been suggested (Leuf and Cunningham, 2001): - A wiki enables all users to collaboratively edit any existing page or to create new pages, in a simple markup language using a Web browser. It usually allows plain texts but not any other add-ons. - A single page is usually interconnected by other wiki pages on the same wiki site via hyperlinks, if its content is referred to on other pages. This meaningful interconnection easily indicates what other wiki pages are available on the same wiki site. - A wiki seeks to involve its visitors in an ongoing process of creation and collaboration. Most wiki sites have a set of relatively stable visitors who can contribute regularly to the
Chapter 2: Literature Review 40
Web content.
As a collaborative encyclopaedia, Wikipedia is now the best known and the most successful wiki site. Wikipedia is an open-content encyclopedia attempting to collect and summarize all human knowledge. While Wikipedia is recognized as the largest and most popular general reference work on the Internet (Tancer, 2007), it is also criticized for systemic bias and inconsistencies (Boyd, 2005).
2.4.3 Social Networking
A social-networking site accommodates an online community that is specially focused on connecting people. It allows users to keep track of their existing interpersonal relationships with close friends or business associates and develop new relationships for social and professional reasons. Social networking sites have been experiencing extraordinary growth in Web 2.0 since the concept was introduced in 2002.
MySpace is the most popular social networking site. It was rated as the top website in May 2007, based on market share (beating Google by 1.5%), reported by Hitwise (an Internet monitor which collects data directly from ISP networks). MySpace helps people to build a network of friends and identify friends’ friends. It provides each user with a page to show general information, pictures, blog entries, messages to visitors, and more customization options. The largest user group on MySpace consists of 35-54 year olds. More and more, teenagers and young adults are being attracted to social networking services such as MySpace. Other popular social networking sites include Facebook, LinkedIn, etc.
Chapter 2: Literature Review 41
2.4.4 Social Tagging
A tag is a non-hierarchical keyword or term assigned to an item to describe the nature of the item, so that people can retrieve that item from a huge collection in an easier way. An item in an open system can be tagged by anyone – its creator or a viewer. In the domain of information and knowledge management, an item would be a piece of information. People usually choose tags in an informal manner (not from a commonly agreed-upon taxonomy). The collection of tags used by many users on a website to label many items becomes a folksonomy (a portmanteau for “folk” and “taxonomy”, meaning a “user-generated taxonomy”).
Del.icio.us is a social tagging site to bookmark a user’s favourite web sites, blogs, news stories, etc. Del.icio.us is a great example of a Social Web company using tagging as user-generated content. The tags added by users are searchable and help organize sites, making it easier to retrieve those marked contents or find the contents they want based on what other users have recommended by bookmarking. As an option, users can add descriptive text on tags, which can help clear up the polysemy of a tag for different people. Other popular social tagging websites include Flickr (a popular photo-sharing site using content tagging) and YouTube (a video-sharing website).
2.4.5 Social Involvement in Information Systems
Social Web promotes many-to-many Web services, which means that every user can be both service consumer and service provider at the same time. This service mode has been increasingly accepted in the application field of online collaboration and sharing as it encourages user interaction and contribution. User-generated content is the key to success for many leading Social Web companies. Harnessing collective intelligence can result in smart ideas. Social filtering (by tags) lets users promote valuable content, and flag offensive or
Chapter 2: Literature Review 42 inappropriate material. The wisdom of crowds suggests that a large, diverse group of people can be smarter than a small group of specialists.
Based on the scope of this research, the social interaction features of Social Web will be one of our chief focuses. This includes using social tagging to better markup information items, allowing users to rate and comment on information and knowledge items and other system users, and observing user behaviour to appropriately make suggestions. Some technical considerations such as rich user experience using advanced technology are also considered but are not our main focal points.
2.5 SEMANTIC WEB AND ONTOLOGY
The current Web is syntactic and its online content is only for humans to read and understand. Semantic Web technology is proposed to mark up online content using formal ontologies to enable computers and people to work in co-operation with each other. (Cardoso, 2007). An ontology is the formal conceptualization of knowledge in a certain domain. As a commonly agreed-upon standard, ontologies provide Web content (to be shared and searched) with both contextual and structural information. Appendix E provides an overview of the Semantic Web and some basic issues of ontological engineering. It also discusses the use of ontologies in knowledge management and presents a research project toward collaborative project information management in the AEC industry.
2.5.1 Ontology as Knowledge Representation
The world “ontology” was taken from philosophy, where its original meaning was a systematic explanation of being (Gómez-Pérez et al., 2004). It has become a popular concept
Chapter 2: Literature Review 43 in computer science since the last decade of the 20th century when related to applications in knowledge engineering. Instead of concerning the essence of things as philosophers, ontology engineers in the context of computer science have been more interested in how ontologies can be used to represent reusable and sharable domain knowledge and how they can be used in applications (Gómez-Pérez et al., 2007). Ontologies are now considered a commodity that can be used for the development of a large number of applications in different fields, such as knowledge management, natural language processing, e-commerce, intelligent integration information, information retrieval, database design and integration, bio-informatics, education, and so forth (Gómez-Pérez et al., 2007).
During the evolution of ontology studies, many definitions have been proposed, and ontologies have been observed from different perspectives and focuses. The most quoted definition is that of Studer and colleagues (1998): An ontology is a formal, explicit specification of a shared conceptualization. “Formal” means that the ontology should be machine readable. “Explicit” refers to the meaning of included concepts, and the constraints of using those concepts should be explicitly defined. “Shared” means that the ontology should capture consensual knowledge accepted by the subject domain of interest, while “conceptualization” means that an ontology is an abstract model of phenomena gained by having identified relevant concepts and the relations/constraints of those phenomena.
There are different knowledge representation paradigms for building ontologies. Early ontologies were built using mainly AI modelling techniques based on frames and first-order logic, for example, the Ontolingua ontologies (Gómez-Pérez et al., 2004). In the last few years, description logics (Baader et al., 2003) has become a popular knowledge-representation paradigm and, along with the increasing attention paid to the Semantic Web, some description logics languages such as Web Ontology Language (OWL) have dominated the field. Different knowledge representation paradigms have different
Chapter 2: Literature Review 44 components to describe domain knowledge. Broadly speaking, four basic components are required for representing a domain of knowledge (Gómez-Pérez et al., 2004). - Classes. The major domain concepts are represented by ontology classes. - Relationships. Ontologies usually use binary relations to represent associations. - Axioms. Axioms are sentences that are always true. - Instances. Individuals of domain concepts are modeled by instances.
2.5.2 Ontology Types
Based on the richness of knowledge representation, ontologies can be roughly categorized as lightweight ontology and heavyweight ontology. Lightweight ontologies include only a simple taxonomy of concepts and a small number of associations. They are called lightweight because they are hardly axiomatized. In contrast, heavyweight ontologies are extensively axiomatized and thus represent ontological commitment explicitly. Every heavyweight ontology can have a lightweight version to be more suitable for some applications.
Based on the specificity involved, ontologies can be categorized into four types: generic ontology, core ontology, domain ontology, and application ontology. The concepts defined in generic ontologies are considered to be generic across many fields. As such, they are also called “upper-level ontology” or “top-level ontology”. Core ontologies define concepts which are commonly used across a set of related domains. They are developed in many situations to improve the interoperability of a certain few domains that exchange information frequently. There is no clear borderline between generic and core ontologies. Domain ontologies express conceptualizations that are specific for a particular universe of discourse. The concepts, relations and axioms embedded in the domain ontology are commonly agreed upon by the majority of domain participants. Basically, a domain ontology encapsulates the generic knowledge in that specific domain. Application ontologies are used for specific computing
Chapter 2: Literature Review 45 applications, and put more specific constraints on the reasoner.
2.5.3 Ontology and Publish/Subscribe Systems
It is important to have an ontology to support publish/subscribe systems. First, an event can be semantically annotated by domain (or application) ontology. This will make the content of events more expressive. Second, the ontology can be used as the virtual schema for publish/subscribe systems. An ontology normally encapsulates most domain knowledge, and using it as a pre-defined schema will eventually give the publish/subscribe system “unlimited” capacity to describe publications and subscriptions within the domain for which the ontology was developed. Third, properly using an ontology can make the publish/subscribe system more autonomic. The matching of publication and subscription is not only based on the asserted definitions encoded in the system, but can also be based on the new definition inferred by the ontology because ontologies have the ability to infer new knowledge.
2.6 ROLE REPRESENTATION
People and organizations are related and interdependent in many ways, both inside the AEC industry and across industries, and each of them has one or multiple roles to play in construction projects. In a one-person project it will be pretty obvious who is doing what. However, construction projects normally involve many teams from very heterogeneous systems (for example engineering, government, financial, etc.) and, even within one team, project participants could have very different responsibilities. Therefore, project roles and their responsibilities will not be self-evident and if they are not properly defined and assigned, there could well be chaos and confusion. Significant research efforts have been dedicated to
Chapter 2: Literature Review 46 construction product modelling and construction process modelling. Little work has been done on the modelling of actors and roles in construction knowledge.
2.6.1 Roles in Social Settings
Many role theorists see roles from two different perspectives. Viewed from the functionalist perspective, role is one of the important ways in which individual activity is socially regulated. Roles create regular patterns of behaviour and thus a measure of predictability, which not only allows individuals to function effectively because they know what to expect of others, but also makes it possible for the sociologist to make generalizations about society. Collectively, a group of interlocking roles creates a social institution: the institution of the AEC industry, for example, can be seen as a combination of many roles, including owner, consultant, contractor, supplier, government agency, etc.
Roles as seen from the functionalist perspective, are relatively inflexible and are more or less universally agreed upon. Although it is recognized that different roles interact with each other (client and contractor), and that roles are usually defined in relation to other roles (general contractor and subcontractor), the functionalist approach makes it difficult to account for the variability and flexibility of roles involved, and also makes it difficult to account for the vast differences in the ways that individuals perceive different roles (Michener and DeLamater, 1999). For example, the role of a Project Manager who is representing the owner and controls a whole construction project will have different behaviours and attributes from those of another individual in the role of Project Manager who is working for a consulting firm in charge of architectural design (which is a project for the consulting firm) for this construction project.
Another perspective from which to observe roles is interactionist. A role, in this conception,
Chapter 2: Literature Review 47 is not fixed or prescribed but something that is constantly negotiated between individuals in a tentative, creative way (Goffman, 1961). This perspective emphasizes the interaction between individuals, and defines roles in terms of interactions. If we think about the example of the Project Manager from the functionalist perspective, we can see that the problem could be resolved from an interactionist perspective by focusing on interaction in defining the roles. A Project Manager managing the construction project will interact with the consultant, contractor, government agency, investor, facility users, etc., while a Project Manager managing the design project will interact with peer consultants, client(s), contractor(s), etc.
2.6.2 Approaches to Modelling Roles
The idea of role modelling has been formally discussed for many years in the field of software engineering. The OOram methodology developed by Reenskaug et al. (1996) presents a general approach to modelling objects and object collaborations using roles and role models. But although there appears to be a general awareness that roles are an important modelling concept, until now no consensus has been reached as to how roles should be represented or integrated into the established modelling frameworks (Steimann, 2000). Although the representation of the role concept abounds in the literatures, Steimann (2000) maintains that there are only three major common ways of representing roles: roles as named places of a relationship, roles as a form of generalization and/or specialization, and roles as separate instances joined to an object.
Roles as Named Places Representing roles as named places in relationships acknowledges that “a role is meaningful only in the context of a relationship (Sowa, 1984 and Guarino, 1992)”. Entity Relationship (ER) models now commonly use role names (for example Student or Teacher) to replace relationship names (for example, “is-student-of” or “is-teacher-of”). Falkenberg (1976)
Chapter 2: Literature Review 48 argued in his Object-Role Model (ORM) that objects and roles are the sole primitives and that associations are derived from objects and roles. This role representation method supports the point that role is a context-rich concept and must be situated in a given setting. The “one-to-many” and “many-to-many” relationships also acknowledge that one can play many roles or that one can play the same role several times simultaneously. However, Steimann (2000) believes that this kind of representation fails to account for the fact that roles come with their own properties and behaviours.
Roles as Specializations and/or Generalizations Specialization (subtypes) and generalization (supertypes) are two popular ways to relate types. Steimann (2000) has indicated that neither the role types nor the natural types are a subset of the other. Thus, using both specialization and generalization will confuse people. The ultimate solution is to separate natural types and role types into different hierarchical structures. The problem of separating role types and natural types and making two parallel hierarchical structures is the difficulty of modelling the relationships between two types.
Roles as Adjunct Instances In order to avoid confusion between natural types and role types when representing roles as named places or missing the relationship between roles and natural types, many researchers have treated roles as independent types whose instances are carriers of role-specific states and behaviours, but not of identity. Then an object and its roles are related by a special relation, i.e., “played-by” or “plays.”
2.6.3 Role Representation in the AEC Industry
The functionalist approach still remains a fundamental concept in the understanding of roles, even though it has been criticized for its static perspective. For a setting such as the AEC
Chapter 2: Literature Review 49 industry, which is relatively stable in terms of roles, this functionalist perspective is favoured over the interactionist perspective. The AEC industry is envisioned as a setting in which entities (individual actors and organizational actors) participate in various construction projects, occupying distinct positions. Each of these positions entails a role, which is a set of functions performed by the entity for the particular setting involved. The AEC industry often formalizes role expectations as norms or even codified rules, while entities usually carry out their roles and perform in accordance with prevailing norms, checking other entities’ performance at the same time to determine whether they conform with these norms.
The argument over how to differentiate roles from natural types has never stopped since the concept of role modelling was introduced by (Sowa, 1984). In Sowa’s article, the author distinguishes between natural types “that relate to the essence of the entities” and role types “that depend on an accidental relationship to some other entity”. By defining roles in AEC industry, the social functionalist perspective is employed and the institution is seen as a combination of the architectural, engineering, and construction societies. Each entity holds one or more roles, and each role is associated with expected behaviours (a set of activities) and possesses a grouping of role-specific attributes. Chapter 4 discusses the role representation in the AEC industry in greater detail.
Chapter 2: Literature Review
3 RESEARCH METHODOLOGY 3
he methodology of this research has been articulated in an objective-centric approach Tas shown in Figure 3-1. The methodology is broken down for each of the three main deliverables presented in Chapter 1. Each objective is associated with a set of research methods and software tools, a deliverable, and an evaluation scheme. For the development of the domain-level ontology, AR-Onto, topics reviewed include KM, LO, knowledge representation in the AEC industry, existing vocabularies or ontologies applicable to this research, actor/role representation, and Semantic Web and ontology. The benchmarking of ontological engineering topics has been studied. The developed AR-Onto was evaluated through competency question review, automatic consistency checking, and interviews with domain experts. The CIKP-Onto was developed on top of the AR-Onto using the similar input along with the benchmarking on ontology reasoning tools and the experience gained from observing a real construction project, the St. Clair project. The delivered CIKP-Onto
50 51 was evaluated by similar tools used for the AR-Onto. The input of developing the CIKP framework includes the CIKP-Onto as the core component to support semantics and the study of the topics relating to human communication, publish/subscribe systems, Social Web, as well as benchmarking on requirement analysis. The developed CIKP framework was evaluated through requirement conformity assessment, automatic reasoning, and focus group study. Although the development of a prototype software is not the core component of this research, it will help to further validate and verify the developed CIKP framework.
Literature Review
Knowledge Management Actor-Role Learning Organization Ontology KR in the AEC Industry (Domain Level) Evaluation Competency Existing Ontologies Question Review Actor-Role Representation Automatic Consistency Human Communication Checking
Publish/Subscribe System Expert Semantic Web and CIKP Ontology Interview Ontology (Application Level) Social Web
Benchmarking Ontology Development Methodology
Evaluation Ontology Language
Ontology Editor Requirement CIKP Conformity Ontology Evaluation Framework Assessment Reasoning Tools Automatic Reasoning Requirement Analysis Focus Prototype Group Study Real Case Experience Implementation St. Clair Project
Legend: Input Objectives/Deliverables Evaluation Tools
Figure 3-1: Research Methodology Overview
Chapter 3: Research Methodology 52
3.1 METHODOLOGY FOR THE DEVELOPMENT OF THE AR-ONTO
Figure 3-2 shows the methodology and major activities involved in ontology development. There are six steps in the process: the definition of application scenarios and scope, the development of an ontological model for actors and roles, the development of competency questions, ontology building, ontology coding, and ontology evaluation and documentation.
Literature Review
Purpose and Intended Committing Users Extraction of to Definition Domain Upper-Level Answering Concepts Competency Application Ontology Questions Scenario Taxonomy Selection of Definition Building Representing Automatic Philosophical Language Consistency Domain and Modelling Study of Checking Scope Actors and Requirement Domain Incorporation Definition Roles Analysis Relationships of Existing Expert
Ontologies Interview Selection of Ontological Competency Identification Development Model Question of Rules and Coding and Ontology Methodology Definition Definition Axioms Reasoning Documentation
Definition Development Development Ontology of of of Evaluation Scenarios Ontology Ontology Ontological Competency and and Building Coding Model Questions Documentation Scope
Figure 3-2: Methodology for the Development of the AR-Onto
3.1.1 Definition of Application Scenarios and Scope
Literature Review: A broad range of literature review was conducted (see Chapter 2 for further details).
Purpose and Intended Users Definition: The overall purpose of the domain-level ontology, AR-Onto, is to represent a high level of knowledge about actors and roles in the AEC
Chapter 3: Research Methodology 53 industry in terms of their attributes (see Chapter 4). The intended users in general are all industry practitioners but this research emphasizes the needs of professionals more than those of physical workers.
Application Scenario Definition: The domain-level ontology, AR-Onto, is aimed at general knowledge about actors and roles in the AEC industry. Domain-level ontologies encapsulate knowledge that is universally valid for the entire domain and all kinds of applications. To support all possible specific applications, a domain ontology needs to be extendable depending on its intended use.
Domain and Scope Definition: The target domain of the AR-Onto is the architecture, engineering, and construction industry, which is normally referred to as the AEC industry. The AEC industry includes two economic sectors: (1) professional services in architecture, engineering and other related fields and (2) the construction industry.
There is no one single ontology that will meet the requirements of all industry application purposes or satisfy all industry practitioners, because there is simply no “perfect ontology” or “optimum concept classification” (Taivalsaari, 1996). Thus, there is a need to limit the scope of the AR-Onto based on its intended use. For concepts to be included in the ontology, the AR-Onto does not try to exhaustively cover every possible actor, role and their attributes, but rather tries to capture the most fundamental concepts that are related to the profiling of major industry actors and roles. For ontology relationships, it is nearly impossible to model every relationship between those concepts, and the focus of the AR-Onto lies in the relationships that are closely associated with actors and roles in terms of their profiles and information/knowledge needs. Axioms in the AR-Onto are minimized to those that are valid for any possible application context in the entire AEC industry and are required to maintain the consistency and integrity of the knowledge represented in the ontology.
Chapter 3: Research Methodology 54
Selection of Ontology Methodology: A number of methodologies and methods for developing ontologies have been reported. There is no single correct methodology to fit all development projects, and there is no one methodology that is superior to another. The selection of one or another totally depends on the characteristics of the ontology to be developed. Also, the process of ontology development is usually iterative (Noy and McGuinness, 2002).
Gómez-Pérez et al. (2004) have summarized several methodologies with significant influence in ontology development (see Appendix E). This research has adopted Grüninger and Fox’s methodology, because it covers relatively more ontology development activities than most other methodologies. Fernández-López and Gómez-Pérez (2002) indicate that none of the approaches cover all ontology development processes. Some methodologies are only used by the research groups that have proposed them, and thus are not examined or evaluated broadly. Few of the existing methodologies have the specific tools required to support the practices in question.
3.1.2 Development of an Ontological Model
Philosophical Study of Actors and Roles: The fundamental reason for differentiating actors and roles in the AEC industry and their philosophical implications has been carefully studied. Any individual or organization could play multiple roles in different contexts at the same time, and those roles represent different needs for information and knowledge. Steimann (2000) has presented 15 features for distinguishing roles from actors, and this research has applied a part of those features to defining roles in the AEC industry.
Ontological Model Definition: An ontological model describes the semantic relationships
Chapter 3: Research Methodology 55 among the main concepts in a given knowledge domain. The AR-Onto is compliant with an upper-level ontology as defined by (El-Diraby, 2008) which specifies a domain ontology for construction knowledge.
3.1.3 Development of Competency Questions
Requirement Analysis: Based on the motivating scenarios discovered, requirement analysis was conducted before the actual development of the AR-Onto. In this research, requirement analysis is used as a tool in understanding the industry’s needs in modelling actors and roles.
Competency Question Definition: Based on the result of requirement analysis and on application scenarios, a set of informal competency questions will be defined in natural language. These competency questions play the role of a type of requirement specification against which the ontology can be evaluated (Gómez-Pérez et al., 2004). According to the methodology specified by Grüninger and Fox (1995), competency questions should be designed to reflect the queries at different levels of complexity, i.e., the questions should be able to be decomposed into more specific questions, or recomposed to form more general questions. In addition, the answer to one question should be able to be used to answer more complex questions.
3.1.4 Ontology Building
Extraction of Domain Concepts: The concepts defined in the ontology are extracted from a broad source of literatures and its analysis, the experience and knowledge of the developer (the author), and the informal competency questions and their answers. Those concepts will be formally represented and defined in the ontology. The terminology extracted is formally represented in a first-order language.
Chapter 3: Research Methodology 56
Taxonomy Building: A taxonomy in ontology is a hierarchy of concepts. The creation of the AR-Onto taxonomy involves two tasks: (1) the identification of major actors, roles, and their attributes, and (2) the organization of those identified concepts into a proper hierarchical structure.
Extracting concepts from competency questions is important but not enough. Competency questions aim at identifying major types of concepts and their superclass-subclass relationships, but there are many more concepts that should be included in the ontology content. Besides the competency questions, the concepts in the taxonomy are obtained from the following two sources: - Literatures in the AEC industry. This includes the textbooks of introductory knowledge of the AEC industry, the literatures published by professional institutes such as the Project Management Institute (PMI) and the Construction Industry Institute (CII), and academic and technical articles published by journals or conferences. - National standards of occupational and industrial classifications. There are some national standards to categorize occupations and industries. The AR-Onto includes the concepts specified in those standards, given that they lie within the scope of this domain-level ontology. Chapter 4 has more information about the reference standards used in the development of the AR-Onto.
There are three methods used to identify and organize concepts into hierarchical structures: the top-down approach, the bottom-up approach, and the middle-out approach. A top-down approach starts by defining the most general concepts and then moves down to the most specific concepts. On the other hand, a bottom-up approach starts by defining the most specific concepts and subsequently groups these specific concepts into general ones (Noy and McGuinness 2002). The middle-out approach combines the two and starts from the most
Chapter 3: Research Methodology 57 relevant to the most abstract and most concrete concepts. When identifying concepts, the middle-out approach is employed, because this is more intuitively sound given the nature of the AEC industry. When organizing concepts into a hierarchical structure, the top-down approach is preferred because the AR-Onto taxonomy mainly follows the structure of the national occupational and industrial classification structures and the top-down approach will match the two structures easily.
Modelling Domain Relationships: A formal technique proposed by Yoo and Bieber (2000), Relationship Navigation Analysis (RNA), is used in relationship modelling for the domain actors and roles. RNA provides system analysts with a systemic technique for determining the relationship structure of an application, helping them to discover all potentially useful relationships by conducting a formal relationship analysis. Three levels of analyzing relationships between elements have been suggested: using a generic relationship taxonomy, domain- independent categories and domain-specific categories.
Identification of Axioms: Axioms are sentences that are always true in the possible application scenarios. There are two purposes for defining axioms: - To protect the integrity and consistency of the ontology. For example, it is generically valid that any individual actor (a person) has one and only one age. The system would automatically find the conflict in the cardinality if this axiom were properly defined and an instance of an actor class were assigned two ages. - To constrain the characteristics, functions or behaviours of ontology concepts. For example, it is normally true that an Architect possesses the skill of architectural design at the entire AEC domain level, regardless of the application contexts. The ontology would constrain the definition of any Architect in terms of the skill of architectural design if this axiom were accepted and implemented in the ontology.
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In order to identify all necessary axioms, every concept of the AR-Onto was carefully examined in terms of its domain-independent factor and domain-specific factor. All accepted axioms and rule are first written in natural language and are categorized into three groups based on the nature of constraints: cardinality constraints, descriptive constraints, and function/behaviours constraints. Later, those axioms are defined as first-order sentences, using the predicates of the ontology. Eventually those first-order clauses are encoded in either the OWL file or the SWRL file (see Section 3.1.5 for more details).
3.1.5 Ontology Coding
Committing to Upper-Level Ontology: Because the AR-Onto is a knowledge model at the domain level for actors and roles in the AEC industry, and because there are similar models in existence for other major entities such as processes and products, an upper-level ontology is required to clarify the common ontological representation of the AEC industry. In fact, the AR-Onto and its sibling ontologies have followed a common upper-level ontology as proposed in (El-Diraby, 2009) where the terms and structures to represent the subject domain has been specified.
Selection of Ontology Representation Language: It has been pointed out in the Appendix E that OWL is the most mature and practical ontology representation language. OWL has a three-layered structure (Antoniou and Harmelen, 2004): - OWL Full. OWL Full uses all the OWL language primitives. It also allows for a combination of these primitives in arbitrary ways with RDF(S). OWL is fully upward-compatible with RDF, both syntactically and semantically. This means that any legal RDF document is also a legal OWL Full document, and that any valid RDF(S) conclusion is also a valid OWL Full conclusion. - OWL DL. OWL DL (short for Description Logic) is a sublanguage of OWL Full which
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restricts how the constructors from OWL and RDF may be used: OWL DL includes all OWL language constructors, but they can be used only under certain restrictions (for example, number restrictions may not be placed upon properties which are declared to be transitive). OWL DL permits efficient reasoning support, but loses full compatibility with RDF. - OWL Lite. The further restriction on OWL DL by limiting the available language constructors results in OWL Lite. For example, OWL Lite excludes enumerated classes, disjointness statements, and arbitrary cardinality. OWL Lite was originally intended to support those users primarily needing a classification hierarchy and simple constraints.
Based on the requirement analysis, the OWL DL has been selected as a proper implementation language for the domain-level ontology, AR-Onto, while the application-level ontology, CIKP-Onto, will be implemented in OWL Full because it requires more complicated axioms and rules.
Incorporation of Existing Ontologies: As a part of ontology-development methodology, existing ontologies that are related to the ontology under development should be examined to determine how to use, and whether it is necessary to use, partially or fully, one or more existing ontologies. No existing ontology was integrated within the AR-Onto as none was deemed to be necessary. However, during the taxonomy-building stage, some existing vocabularies that are related to the AEC industry were indeed considered and partially integrated into the AR-Onto where the concepts fit its scope. Those vocabularies are: - British Standard 6100 (BSI, 2002). BS6100 is a “Glossary of Building and Civil Engineering Terms” which covers all topics that are the responsibility of the Sector Committee for Building and Civil Engineering. - Uniclass (CPIC, 1997). Uniclass is another classification scheme which is intended to organize library materials and to structure product literature and project information for
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the construction industry. - IFC (IAI, 2008). Industry Foundation Classes provides a foundation for the shared project model and specifies classes of things in an agreed manner that enables the development of a common language for the AEC industry. - Building and Construction XML (Lima et al., 2003). Under a European community funded project called “e-Construct,” an XML schema taxonomy and dictionary are being developed, called bcXML (Building Construction eXtensible Markup Language), which is focused on construction products, resources, work methods and regulations.
Coding and Reasoning: A set of available ontology development tools has been studies (see Appendix E for more details) and Protégé (Ver. 3.4) has been selected as the development environment of the AR-Onto, based on the overall evaluation. The AR-Onto concepts are encoded in the Protégé as Classes and the AR-Onto relationships are encoded as Properties. They are two kinds of properties: - Object Property. A property is considered as an object property if the range (object) of the relationship (predicate) to be represented is a concept. For example, the relationship “award” is considered as an object property. “An owner awards a contract,” because the range (object) of the predicate is a “contract”, which is a concept according to the AR-Onto. - Datatype Property. A property is considered as a datatype property if the range (object) of the relationship (predicate) to be represented is a kind of data. For example, the relationship “name” is considered as a datatype property. “An architect has name John Smith,” because the range (object) of the predicate is “John Smith” which is a string, a type of data.
The axioms are coded either in OWL through defining restrictions in the Protégé or in SWRL (Semantic Web Rule Language). Protégé restrictions are capable of dealing with simple
Chapter 3: Research Methodology 61 axioms. For example, there is an axiom that states that “for any given individual actor, there is one and only one age.” Then this axiom can be expressed in Protégé by clearly restricting the cardinality of the property “age”. For some complex axioms, we have to translate the formal first-order sentences into SWRL. For example, there is an axiom in the domain level that states that “if a role is played by an actor, and this role is affiliated with a certain project, then the playing actor is affiliated with the same project”. The formal first-order sentence for this axiom (Figure 3-3) is coded in the SWRL which can then be reasoned out by the Jess reasoning engine.
∀x, y, z Role(x) ∧ Actor(y) ∧ Pr oject(z) ∧ is _ Played _ By(x, y)
∧ has _ Pr oject _ Affiliation(x, z) → has _ Pr oject _ Affiliation(y, z) Figure 3-3: Sample of Axioms
Jess is used in Protégé as a rule engine and scripting environment written entirely in Sun’s Java language. Jess is capable of reasoning rules written in the SWRL based on the OWL ontology and of generating conclusions (new knowledge) to be expressed as new assertions in the OWL file. After embedding the Jess reasoning engine in the Protégé development environment, ontologists can import the OWL ontology and rules written in SWRL into Jess and run the reasoner, then export the new assertions into the OWL ontology to be displayed in the Protégé environment.
3.1.6 Ontology Evaluation and Documentation
Ontology evaluation is a judgment of the ontology’s content with respect to a particular frame of reference. Appendix E mentioned three terms used in ontology evaluation: ontology verification, ontology validation, and ontology assessment. The first two of the three can be conducted by a technical developer, and the third is usually treated as user evaluation and needs to be conducted by potential users. In this research, the technical evaluation is
Chapter 3: Research Methodology 62 conducted by answering competency questions and by conducting an automatic consistency check. User evaluation is performed through a series of interviews with domain experts to judge the ontology’s content from the user’s perspective.
Answering Competency Questions: There are two purposes for identifying a set of competency questions in ontology development: extracting domain concepts at the beginning and evaluating the ontology after finishing. As this research has adopted the methodology specified by Grüninger and Fox (1995) which suggested using competency questions as one of evaluation methods, this task was performed in the ontology evaluation stage.
Automatic Consistency Checking: Answering competency questions is a way to check the completeness of an ontology or to verify the fulfillment of application scenarios, but it is very weak in validating the consistency of an ontology. Consistency refers to whether it is possible to obtain contradictory conclusions from valid input definitions. Many ontology development tools provide one or more than one approach to automatically check for consistency through reasoning engines. Based on the description (conditions) of a class, the reasoner can check whether or not it is possible for the class to have any instances. A class is deemed to be inconsistent if it cannot possibly have any instances. Protégé version 3.4 is shipped with a default reasoning engine called Pellet (Version 1.5.1) and users can import other compatible reasoning engines such as Racer or Fact++. In this research, the default reasoning was used to perform consistency checking for the AR-Onto.
Expert Interview: As soon as the technical evaluation has been conducted by the ontology developer, a series of interviews with domain experts will be scheduled to evaluate the ontology from the perspective of potential users. The interviewees are individuals who cover the major roles in the AEC industry: contractors, owners, designers, project managers, government regulators, etc. The expert interview will be conducted in three stages:
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- A 10-page research summary is sent to each participant two weeks before the interview, so that they can have some basic understanding about this research and the concept of ontology before the interview. - The first part of the interview is a 30-minute presentation to further introduce the ontology. This stage gives interviewees opportunities to ask questions about ontology and make sure they are clear about this concept – one that confuses most people at the beginning. - The interviewees will finish a questionnaire in the second part of the interview. The completed questionnaire will then allow for an assessment of the ontology in terms of representativeness, completeness, conciseness, and ease of use.
Ontology Documentation: The last step of ontology development is documentation, which means a detailed recording of the ontology concepts, definitions, relationships, axioms, evaluation results, and any comments collected in interviews with the experts involved.
3.2 METHODOLOGY FOR THE DEVELOPMENT OF THE CIKP-ONTO
This section discusses the methodology for the development of the application-level ontology CIKP-Onto, which is used to model communication among major industrial actors and roles in terms of their needs for information and knowledge. The methodology used for the development of the AR-Onto is still applicable to the CIKP-Onto, because essentially, both are ontologies, but ones that differ in both scope and level. The AR-Onto focuses on actors and roles at the domain level while the CIKP-Onto covers a broader scope in order to model the communication of information and knowledge at a more finely tuned level. This section addresses the aspects that are different from those in the development of the AR-Onto.
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Definition of Application Scenarios and Scope The purpose of developing the CIKP-Onto is to build an interoperable knowledge representation to cover the knowledge of communication among major actors and roles in the AEC industry. The direct users of the CIKP-Onto would be the developers of computer applications that aim at information and knowledge communication, and the indirect users would be people who use those computer applications. It has been envisioned that the intended indirect users would be knowledge-intensive workers who need to communicate information and knowledge frequently in day-to-day work, such as design professionals and project managers.
In order to comply with the intended use of the CIKP-Onto, there is a need to define the scope of this ontology in terms of the concepts, relationships and axioms that must be covered. - Concepts: The CIKP-Onto inherits the concepts of actors and roles from its domain-level counterpart, the AR-Onto. The inherited concepts of actors and roles are tailored to better fit the requirements of the CIKP-Onto by removing the concepts that are not within the scope of this application-level ontology. Because the subject of communication is either information or knowledge which is manifested by a type of media as a kind of product in the AEC industry (for example, a set of design drawings as the product of a technical design process), this ontology should model the concept of Products in general and Knowledge Item Products in particular. In addition, other major entities specified in an upper-level ontology should also be incorporated when necessary. - Relationships: The relationships in an application-level ontology are more specifically geared to describing the activities (functions) or characteristics (descriptions) associated with concepts in a particular application environment. The relationships included in the CIKP-Onto may not be required in other application scenarios but are necessary for communicating information and knowledge in the AEC industry.
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- Axioms: Similar to the relationships at the application level, axioms in an application ontology may not be valid in other application environments, but will be valid in that one specific application. The axioms included in the CKIP-Onto are mainly used with reference to the needs for information and knowledge on the part of actors and their roles in the specific application involved.
Development of an Ontological Model In order to understand the mechanism of technical communication, a set of class diagrams has been developed by UML (Unified Modelling Language) to represent the communication of information and knowledge.
Ontology Building Building the CKIP-Onto includes the four tasks involved in building the AR-Onto, but does so with a different emphasis. The extraction of most concepts that are to be formally defined in the ontology needs to be done with particular reference to the communication of information and knowledge. It is important to identify concepts of Knowledge Items.
As the CIKP-Onto inherits the concepts of actors and roles from the AR-Onto, the taxonomy structure is also inherited. Within this structure, these two concepts (Actor and Role) only involve a kind of fine-tuning/tailoring to maintain the concepts that are required by the CIKP-Onto and remove unnecessary concepts. Taxonomies for other concepts such as Knowledge Items are to be developed.
Relationship modelling in the AR-Onto focuses on the relations associated with actors and roles, while in the CIKP-Onto relationships should be expanded to cover all major entities in general and the concepts of knowledge items in particular. In addition, the approach proposed by Yoo and Bieber (2000) stops at the level of domain-specific relationships, but it is
Chapter 3: Research Methodology 66 necessary to take a further step and bring relationship modelling up to the application level of the CIKP-Onto.
In the development of an application-level ontology such as the CIKP-Onto it is necessary to develop as many axioms as may be required by the specific application purposes. In this case, many axioms will be created to constrain or infer the needs of actors and roles for information and knowledge.
Ontology Coding The CIKP-Onto is encoded in OWL Full language, and Protégé version 3.4 is used as the developmental environment (an editor). Again, the SWRL is used to formally encode complex axioms and the Jess is used as the reasoning engine to infer new knowledge based on existing assertions in the OWL file and rules expressed in SWRL.
3.3 METHODOLOGY FOR DEVELOPING THE CIKP FRAMEWORK
This section addresses the methodology for developing an information system framework, a Construction Information and Knowledge Protocol/Portal, designed to coordinate the flow of information and knowledge based on the needs of actors and roles, by incorporating within it three IT technologies: Semantic Web technology, Social Web concepts, and a Publish/Subscribe system. Figure 3-4 outlines the major steps involved in proposing the framework: requirement analysis, social involvement, basic function definition, and framework evaluation. Prototype implementation will be outsourced to a third party, and is therefore not a part of this work.
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User Profile Definition
Publish Literature Subscribe Review Requirement Intended User Browsing Conformity Identification Knowledge Assessment Literature Item Application Review Automatic Scenarios Browsing Reasoning Social User Technical Interaction Focus Group Approach Approach Manage CoP Study
Social Requirement Basic Function Framework Prototype Involvement Analysis Definition Evaluation Implementation Definition
Figure 3-4: Methodology for Proposing the CIKP Framework
3.3.1 Requirement Analysis
Literature Review: Literatures about existing information management systems, including project management systems, are carefully examined, and the gap between the existing systems and industry needs has already been identified.
Intended User Definition: The intended user of the CIKP framework is the indirect user of the two ontologies, i.e., all industry practitioners in general. In fact, in the AEC industry, some actors and roles (for example, professionals in design and management) have much stronger needs for information exchange and knowledge sharing than others (for example, physical field workers), and therefore, this research pays more attention to those information- or knowledge-intensive users.
Application Scenarios: It is necessary for any information system to first identify its possible application scenarios before design work begins. Three application scenarios have been identified for the CIKP framework (see Chapter 6 for further details):
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- The basic motivating scenario of this research envisions that various individuals and organizations will coordinate actions with each other by effectively exchanging project information and knowledge. - Another motivating scenario relates to sharing domain-specific knowledge. - The last motivating scenario is to support knowledge learning and sharing through the creation of communities of practice (CoPs) or virtual teams.
Technical Approach: In order to fulfill the requirement of three application scenarios, a series of technical approaches have been examined, and three IT technologies have been selected to be combined and integrated into the CIKP framework: - Semantic Web technology and ontologies. As an explicitly defined knowledge representation, the CIKP-Onto will effectively support the semantic aspect of communicating information and knowledge. - Publish/Subscribe systems. The publish/subscribe communication paradigm is an effective way in which to meet the communication requirements defined in the application scenarios for this highly fragmented and dynamic industry, due to its ability to decouple publishers and subscribers. - Social Web concepts. The advantage of social involvement will enrich the semantics of any information or knowledge published and will help to establish virtual communities among users.
3.3.2 Social Involvement Definition
Literature Review: A broad range of literatures about the Social Web and Web 2.0 has been examined to benchmark the key principles of social websites that has led many web applications to be successful (see Section 2.4 and Appendix D for further details.) A set of Social Web approaches has been identified and will be utilized in the CIKP framework.
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Social Interaction Approach: Based on the suitable social web concepts discovered in the literature research, the following social interaction approaches have been employed in the CIKP framework: - Social commenting. Every user can comment on knowledge items published by others. - Social tagging. Every user can place tag(s) on any knowledge item or any user’s profile. - Social voting. For each tag or comment placed by other users, every user has the right to express their opinion by voting “agree”, “disagree”, or “flag”.
3.3.3 Basic Functions Definition
Based on the application scenarios and technical approaches proposed, a set of basic functions of the CIKP framework has been identified: - Define profile. Every user will be able to define his/her profile in the system by indicating key attributes. - Publish. Every user will be able to share a piece of information or knowledge with others by publishing it into the system. - Subscribe. Every user will be able to subscribe to information or knowledge published by others by defining one or more subscriptions. - Browse. Every user will be able to browse through published items and/or other system users’ profiles. - Manage CoPs. Every user will be able to join/level/manage Communities of Practice (CoPs).
3.3.4 Framework Evaluation
Requirement Conformity Assessment: The service goals and scenarios defined by
Chapter 3: Research Methodology 70 requirement analysis served as a frame of reference and as requirement specifications against which the CIKP framework could be evaluated. All designed functions and associated activities should be carefully examined against the defined services.
Automatic Reasoning: This research will employ the Jess reasoning engine to automatically reason complex axioms and rules defined in the SWRL and generate new assertions in the OWL file. This approach will examine whether the application-level ontology, CIKP-Onto, can well support the services defined in requirement analysis.
Focus Group Study: The CIKP framework is evaluated by a Focus Group (FG) study from a perspective of potential users. A FG is a qualitative research method which is used to gather the opinion of a group of people about a specific topic or area of interest. The reason a FG has been chosen as the evaluation method for the CKIP framework is that the framework is a conceptual structure and mainly specifies required functions; as such it is very subjective and hard to validate or verify without a real case study. A FG study will assess the CIKP framework from the perspective of its potential users and the resulting feedback will be used to improve its functionality.
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4 DOMAIN ONTOLOGY FOR ACTORS AND ROLES 4
he domain ontology for actors and roles (AR-Onto) is the formal representation of Tdomain knowledge about actors and their roles in the construction industry. The AR-Onto defines the majority of individual actors and organizational actors in terms of the attributes closely related to their definitions. The AR-Onto also defines the roles that each actor could play in a variety of settings in the AEC industry with respect to the attributes pertaining to those roles. This domain ontology specifies the relationships and axioms at a general level for the AEC industry and leaves the maximum room and flexibility possible for further extension in various directions. The AR-Onto is intended for use as a core model for actors and roles in the AEC industry, as well as a main framework for modelling actors and roles in any given application environment.
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The domain ontology described here has been developed for the AEC industry, and as such, it automatically covers the majority of the concepts in the industry. The fact is that there are many other domains closely related to the AEC industry such as, for example, the legal domain and the management domain. The ontology includes the concepts from those closely related domains that are used in the AEC industry. The AR-Onto models our existing knowledge about those concepts from a construction-industry perspective, and in varying levels of detail.
The AR-Onto has been developed for general applications involving industry actors and roles, with a focus on information and knowledge communication. In order to accommodate other application scenarios, the structure of AR-Onto has been designed in such a way as to allow flexible extension for any purpose by covering the majority of actors and roles that are commonly used in the AEC industry.
4.1 REQUIREMENT ANALYSIS
Existing modelling efforts on the actors and roles in the AEC industry are limited and polarized. Some introductory explanation of actors/roles could be found in textbooks which are mainly used for construction engineering students or people new to this area. For example, Murdoch and Hughes (2000) provide general descriptions of the main roles in construction processes. Since textbooks tend to provide overviews of the construction industry, the descriptions are normally informative, but lack analytical details (Hughes and Murdoch, 2001). Some detailed research presented in these literatures goes into considerable depth about only one or just a few roles. Examples include Cherns and Bryant (1984) who described an in-depth study of the role of clients, Hodgson and Jeffry (1999) who examined the role of the designer under civil engineering design-build contracts, and Price (1994) who
Chapter 4: Domain Ontology for Actors and Roles 73 analyzed the role of subcontractors in construction processes. All the above-mentioned literatures discuss actors/roles in the AEC industry either very broadly and in little depth, or very elaborately but with too narrow focuses. There has been no systematic modelling to expound on the different roles of various industry actors in terms of their behaviours and associated attributes.
The general consensus concerning most of these literatures is that there is no full consensus on the definition of construction actors/roles in terms of their behaviours and attributes. There is a need to develop a widely accepted and sufficiently effective model to encapsulate knowledge of actors and roles in terms of their behaviours and attributes. This need lays the following requirements on the AR-Onto: - The ontology needs to be able to cover the majority of actors and their roles in and related to the AEC industry. - The ontology needs to be able to model the attributes of actors and roles. - The ontology needs to specify the taxonomical relationships between concepts. - The ontology needs to be able to model the non-taxonomical relationships between actors, roles and other domain entities at the domain level. - The ontology needs to be able to model the actors and roles from different perspectives such as a project-centered view or a process-centered view. - The ontology needs to be able to model actors and roles throughout the life cycle of a general construction project. - The ontology needs to be able to model the profiles of actors and roles using the attributes and relationships defined. - The ontology needs to be able to represent domain-level axioms to ensure its validity and consistency.
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4.2 COMPETENCY QUESTIONS
As the Grüninger and Fox’s (1995) ontology development methodology has been employed, motivating scenarios should first be identified as a set of requirements that have to be satisfied after the ontology is formally implemented. There are two basic application scenarios for the domain ontology AR-Onto: - Various individuals and organizations can be profiled in terms of their intrinsic attributes. They are treated as actors (or stakeholders) playing different roles which have different perspectives and represent different interests. - Any actor can be assigned one or multiple roles which are profiled by extrinsic attributes. A single user of any possible application that adopts the AR-Onto domain-level ontology is the combination of an actor profile and one or multiple role profiles.
Based on the requirement analysis and the application scenarios of the domain-level ontology, a set of informal competency questions, expressed in natural language, have been identified as a type of requirement specification. Informal competency questions are used for two purposes: (1) to extract the content of the ontology, including concepts, relationships between concepts, and governing axioms and rules, and (2) to evaluate the domain ontology by examining whether the ontology is able to answer those questions. Due to the relatively large scope of the AR-Onto, competency questions have been categorized into two types: competency questions focusing on extracting ontology content and competency questions focusing on evaluating the ontology. As has been said before, each competency question carries the dual role of extracting content and of evaluating the ontology, and the two roles serve to emphasize the different focuses.
4.2.1 Hyponymy and Partonymy Questions
These questions define the major concepts of actors and roles in the proposed domain-level
Chapter 4: Domain Ontology for Actors and Roles 75 ontology, as well as the “is-a” and “part-whole” relationships between concepts. Hyponymy represents an “is-a” relationship and links a superclass concept to a subclass concept. For example, an Electrical Engineer is an Engineer. Partonymy represents a “part-whole” relationship and links a concept with concepts representing its components. For example, Architectural Design Process is a part of Technical Design Process. Table 4-1 shows the competency questions that have been mainly used to extract ontology content and the competency questions that have been mainly used to evaluate the ontology.
Table 4-1: Competency Questions for Actors and Roles Competency Question Competency Question (used to extract ontology content) (used to evaluation ontology) What actors/roles are closely involved in Given that there is a certain actor/role in the construction projects? What actors/roles are AEC industry in any possible context, can it not closely involved in construction projects be located it in the ontology? but are related to the construction industry? What are possible synonyms of a given Given that there is a synonym for a certain actor/role? actor/role, is it possible to identify it in the ontology? What are the major types of actors/roles in Given that a certain actor/role belongs to a the AEC industry? How are they specific group, is it possible to verify it in categorized into groups? the ontology? What are the specialized or generalized Given that there is a generalized/specialized types of a given actor/role in the type for a certain actor/role, is it possible to construction industry? find it in the ontology? What are the sub-parts of a given entity, if Given that a certain entity is a sub-part of applicable? Is a given entity a part of another entity, is it possible to identify that another entity? in the ontology? What are the definition and its source of a Is it possible to find/obtain the definition given actor/role? and its source from the ontology for a given actor/role?
4.2.2 Multi-view (Modality) Questions
Most concepts in a subjective domain of knowledge such as that of construction are
Chapter 4: Domain Ontology for Actors and Roles 76 multidimensional, which means that they can be viewed, described and classified from many perspectives. For example, organizations as representative of a kind of actors could be categorized according to ownership (public organizations, semi-private organizations, or private organizations), the business objective (for-profit organizations or non-profit organizations), or the economic sector (architectural and engineering service organizations, general contracting organizations, construction material manufacturing organizations, etc.) This proposed domain-level ontology adopts the concept “modality” to deal with the multi-view of actors and roles, i.e., all actors and roles are categorized by the most commonly
Table 4-2: Competency Questions for Modalities Competency Question Competency Question (used to extract ontology content) (used to evaluation ontology) What are the business types of Given that there is a certain non-government organizations? non-government organization belonging to a specific business type, is it possible to identify that in the ontology? What are the constitutional positions of Given that there is a certain government government organizations? organization at a specific constitutional level, is it possible to identify that in the ontology? What are the necessary educational levels of Given that there is an educational individuals? background requirement for a certain individual, is the ontology able to discern it? What are the possible contexts of roles? Given that a certain role belongs to a specific context, is it possible to identify that in the ontology? What are the ownerships of organizations? Given that a certain organization has a specific ownership, is it possible to identify that in the ontology? Is a role internal or external to a given project or process? Does a given role belong to a specific life cycle stage for a given project, product, process, event, organization, etc.? Does a given actor or role require filed work? Does a given actor (individual or organization) belong to a specific domain of expertise or practice that requires licensing or the possession of a specific set of expertise? Does a given organizational actor belong to a specific economic sector?
Chapter 4: Domain Ontology for Actors and Roles 77 accepted perspective at the top level and then different modalities are created to accommodate a diversity of views (see Section 4.8 for more details about concept modalities). Table 4-2 shows modality questions.
4.2.3 Attribute Questions
Attributes are used to reflect the essential features of being a certain entity, i.e., an actor or a role in the AR-Onto. The attribute questions will extract necessary concepts to describe actors and roles and evaluate the ontology after it is formulated. Those competency questions are shown in Table 4-3.
Table 4-3: Competency Questions for Attributes Competency Question Competency Question (used to extract ontology content) (used to evaluation ontology) What are the intrinsic attributes of industry Given that there are attributes that are participants (actors)? intrinsic to an individual, is it possible to identify them in the ontology? What are the extrinsic attributes of industry Given that there are attributes that are participants (roles)? extrinsic to an individual, is it possible to identify them in the ontology? What attributes are shared by actors and Given that there are certain attributes that roles, if existing? are suitable for both actor(s) and role(s), does the ontology recognize this situation? What are the abstract/physical attributes of Given that there are certain individual actors and organizational actors? abstract/physical attributes of an actor, is it possible to identify them in the ontology? What are the attributes of roles? Given that there are certain attributes of role, is it possible to identify them in the ontology? What are the specialized or generalized Given that there are generalized/specialized types of a given attribute? types of a certain attribute, is it possible to find them in the ontology? What are the instances of a given attribute Given that an attribute has some values, is it concept? possible to find them in the ontology?
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4.2.4 Cross-Tree Relationship Questions
The relationships mentioned in this section refer to the linkages between concepts which are not in the same hierarchical path in a taxonomy structure, i.e., concepts that are not related by a hyponymy (is-a) relation. These “cross-tree” relationships enrich the representation of domain knowledge by describing the behaviours of a concept and situating it with respect to other concepts. Table 4-4 shows competency questions of relationships.
Table 4-4: Competency Questions for Attributes Competency Question Competency Question (used to extract ontology content) (used to evaluation ontology) What are the functions of actors/roles? Given that there are certain functions related to a specific actor/role, is it possible to indentify it in the ontology? What other concepts are related to a given Given that an entity is related to a certain actor or role? And how are they related? actor/role, is it possible to identify the relationship in the ontology? What are the specialized or generalized Given that there are generalized/specialized types of a given relationship? types of a certain relationship, is it possible to find them in the ontology? What relationships are shared by actors and Given that there are certain relationships roles, if existing? that are suitable for both actor(s) and role(s), does the ontology recognize this situation?
4.2.5 Axiom Questions
A set of axioms in ontologies is used either to constrain the attributes or behaviours of actors and roles or by deduction to yield new knowledge about actors and roles. The proposed AR-Onto includes the axioms relevant to domain-level knowledge which is valid for all possible applications in the AEC industry. Table 4-5 shows competency questions about axioms.
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Table 4-5: Competency Questions for Axioms Competency Question Competency Question (used to extract ontology content) (used to evaluation ontology) Who can play what roles in the construction Given that an actor can only play certain industry? roles in a specific context, is it possible to identify that in the ontology? What attributes are necessary (or optional) Given that certain attributes are mandatory to define actors and roles? (or optional) to a specific actor/role, is it possible to identify that in the ontology? What are the constraints on actors and roles Given that there are certain constraints on a for a given process, event, project, product, specific actor/role for a process, event, or resource, in the general setting of the project, product or resource, is it possible to industry? identify that in the ontology? What are the constraints on the behaviours Given that there are certain constraints on a of actors/roles? specific relationship, is it possible to identify them in the ontology? What are the constraints on the attributes of Given that there are certain constraints on a actors/roles? specific attribute, is it possible to identify them in the ontology?
4.3 MAIN ONTOLOGICAL MODEL
An ontological model describes the semantic relationships among the major concepts in a given knowledge domain. The AR-Onto follows the ontological model of construction knowledge as defined in (El-Diraby, 2009). As illustrated in Figure 4-1, that model recognizes three main elements: Universals, Individuals and Attributes. As the representation of main concepts, Individuals are divided into two main categories: Entities and Substances. Systems and Modalities are two elements driven from Entities, Substances and Attributes to dynamically categorize concepts. Systems are used to aggregate concepts that define a specific domain and Modalities are used to generate or accommodate different perspectives of viewing concepts. The model also recognizes a set of Abstract Concepts such as Time, Space, Quality, etc. whose nature is constantly discussed by philosophers. The domain of abstract concepts is common to many knowledge domains, and the model proposed by
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El-Diraby (2009) just links to it without interference in its internal structure.
Figure 4-1: Ontological Model of Construction Knowledge (El-Diraby, 2009)
Entity is the core concept used by many ontologists in modeling the separate and distinct existence of a subject domain. The concept of ontology in computer science and information science originates from philosophy where ontology is the study of the nature of being, existence or reality in general, with its scope extended to a formal representation of a set of concepts within a domain and the relationships between those concepts for a better capacity for reasoning. As such, entities in a knowledge domain are the core elements to study to understand how they can be grouped and subdivided in a hierarchical structure based on their similarities and differences, how they can be related with respect to the nature of the subject domain, and what rules constrain those relationships.
In El-Diraby’s model, entities are divided into seven categories: Action, Actor, Role, Project, Product, Resource, and Constraint. They are modeled as an extended IDEF0 input-output
Chapter 4: Domain Ontology for Actors and Roles 81 format (see Figure 4-2). Action as the core of the model is divided into two types: Event and Process. An Action specifies a set of Roles to be performed by Actors, uses Resources that can be divided into consumable Input and Mechanisms to produce a Product (or advance the life cycle of a product), is constrained by a set of Constraints and may belong to a Project. All construction Entities relate to the above-mentioned Abstract Concepts and Substances. A System is the aggregation of all relevant entities, abstract concepts and substances. Everything in the model has a set of Attributes. This research project focuses on ontology development for actors and roles, and there are sibling development projects working on other major entities. Those major entity groups are defined as follows:
constrains plays
Figure 4-2: Ontological Model of Entities (adapted from (El-Diraby, 2009))
- Project: A project is a temporary endeavour undertaken to develop or establish a unique product, service or result. - Action: An action is a predurant component (something that has just happened and has no physical existence) of a project. There are two types of actions: Process and Event. A process occurs across intervals on a timeline, while an event changes the state of the world just at just one point in time.
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- Product: A product is the output of processes and some purposeful events (mainly meetings). It could be a physical outcome (e.g., a building) or a non-physical outcome (e.g., a decision) of a process. - Actor: An actor is an entity who performs in an action. - Role: A role is the collection of dynamic features in a given context. A role and the actor playing the role share the same identity. - Resource: Resource refers to the physical, human, financial, and intellectual means used in the production of goods and services. Resource can also be divided into Mechanism and consumable Input. - Constraint: A constraint is a restriction of the feasible behaviour or attributes of an entity. - Attribute: An attribute is an abstraction or description of a characteristic or property of an entity.
Following this ontological model, a set of rules are established to constrain actors/roles and other major entities. Section 4.4 explains those rules in greater detail. - Roles and actors are parts of a trilogy that includes processes, i.e., a role is played by an actor and situated in a specific context (process). - Roles are more process-oriented than actor-oriented as a role is associated more with functions/behaviours in a context (process). - Roles are a collection of functions that are needed/performed at various stages of a process. - A set of generic stages exist in all processes. These include initiation, planning, execution, evaluation, etc. - A set of generic functional roles are needed for all processes: initiator, planner, executioner, evaluator, etc. - The functional aspect of roles mandates a set of attributes to these roles – for example, required skills, authority, responsibility, liability, etc.
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- While roles are process-centered, actors tend to be centered on the targeted agent (whether a person or an organization). - An actor is skill-dominated in contrast to the function-dominated roles. - An actor augments a set of innate, normally static, attributes at agents such as, for example, skill and age. - Roles are “hats” that actors can wear. One actor can wear multiple hats at the same time, and can drop/take hats dynamically as required.
4.4 MODELLING ACTORS AND ROLES
4.4.1 The Actor-Role Debate
The reason for differentiating Actor and Role is that the needs for information and knowledge of an industrial participant (an individual or an organization) not only depends on who the participant is (the Actor) but also what the participant does (the Role). For example, a Mechanical Engineer who is not embedded in any context (does not participate in any one particular project or process, and is not related to any one organization, etc.) normally cares primarily about general information and knowledge related to his profession such as new technology in mechanics or general professional events. The role of a Mechanical Engineer will vary greatly depending on the different contexts where he/she may be involved. There could be several Mechanical Engineers working on a project with different roles such as a Designer (from a consulting firm), a Supplier (from a manufacturing company), an Inspector (from a government department), or a Contractor (from a contracting firm). Those roles normally have different perspectives on the project, and are associated with different interests, responsibilities, rights, and many other attributes, although they are all played by the same type of actor, which is that of a mechanical engineer. Therefore, understanding Actors and Roles as well as their relationships becomes essential to effective communication in the
Chapter 4: Domain Ontology for Actors and Roles 84 construction industry..
Finding a proper way to define roles in our lives and model their relationship to actors has been something long debated among philosophers, and the debate will continue into the future. Because of the ever-present “fuzziness” between the two concepts, some researchers have adopted the view that actors and roles can only be modelled as a combined entity “actor-role.” This is because it is so hard to find a clear methodology to differentiate between the two concepts. Should we consider a “project manager” to be an actor or should we consider that to be a “role” played by a “civil engineer”. Which of a “designer” and an “architect” should we consider as the actor? Is an “owner” an actor or a role? What about a “client”? Can a design firm be the designer on one project and the project manager on another and a client (or client representative) on a third? This kind of confusion puzzled the author for a quite long time at the beginning of the ontology development process, and it was concluded that there is no perfect resolution to the debate. Thus, we have to accept a “good enough” model – a model that is as well-suited to the AEC industry as is currently possible.
4.4.2 The Actor-Role Model
In this model an Actor is person- or organization-oriented, tends to describe the innate characteristics (for example, qualification or skill) of a person or an organization, while a Role is context-oriented, and tends to describe external attributes (for example, functions or responsibilities) required by a context that are to be assigned to an actor. A “role” represents a situated position of an “actor” with respect to a specific context. The context refers mainly to a process, but could also refer to a product, a project, an event, etc. In other words, the actor-role duality is actually a trilogy of actors and their temporal (assigned) roles for a variety of settings. In short, an actor can assume different roles in different contexts.
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Among all of the settings, process is the most important entity for identifying roles in the AEC industry. The definition of roles and the selection of proper actors to perform them is derived from the context of a process. For example, a general design process requires a set of attributes to be present in the entity performing the design work. The minimum includes design skills, design experience, academic and professional qualifications, etc. If this process is part of a design-build contract in an urban infrastructure project, this context further defines additional (more dynamic) attributes such as liability levels, span of authority, and so on. The former set of attributes defines more of the innate characteristics that have to be present in an individual (or a firm) to assume the role of a designer in general, irrespective of the context. Those attributes cannot be assigned to the actor by the process or the context. The latter set of attributes is more of a floating set of characteristics that can be assigned to any actor (as long as they satisfy the minimum requirements of the innate attributes).
Consequently, this ontology defines actors primarily on their innate attributes. Fundamentally, an Actor’s definition is limited to the most basic attributes and legal characteristics of an entity, and the definition holds irrespective of what is being done. A Civil Engineer will always be a civil engineer. He or she has well-defined, (momentarily) static and innate attributes (skills, expertise, background, academic qualification, for example). The Role that is assigned to him or her depends on the context. This engineer can be assigned the role of Designer, or Project Manager, Construction Manager, Owner, Quality Controller, Safety Consultant, or Permitting Agent based on different contexts. Similarly, an Architectural Service Company can assume the role of a Designer, Project Manager, or Consultant. Regardless of the role assigned to the company, it has a set of static attributes that allows it to play various roles in different contexts (for example, resources, expertise, bonding capacity). These attributes qualify the company to assume different roles in different contexts. Additional dynamic attributes will be assigned to this company when its role is assumed. For example, if the company assumes the role of Contractor, it will be responsible for safety,
Chapter 4: Domain Ontology for Actors and Roles 86 quality and project deliverables. If the company, however, is assigned the role of Constructability Consultant it will have a different set of these dynamic attributes. In short, in this ontology, the concept of Actor is context independent and defined by the most intrinsic attributes, while the concept of Role on the other hand is context dependent and is defined by the most extrinsic attributes.
An Actor is defined through a set of intrinsic attributes. These include professional attributes (such as educational degree, certification level, skills, etc.), logistical attributes (such as address, e-mail, etc.), personal attributes (habits, interests, motivation level, etc.), and performance attributes (productivity, innovation level, etc.) These attributes tend to be static and are associated with the actor for a sustained length of time.
In this ontology, a Role is basically a collection of behaviours, rights, and obligations in a situated context. Roles are viewed as three-dimensional entities. The first dimension represents the stages of a process. Any process is divided into a set of typical stages. These include the initiation stage (where some actors initiate a process), the planning/design stage (where the team responsible for the project defines the scope of the process and develops its action plan), the execution stage (where the process is actually executed), and the evaluation stage. The second dimension represents the basic roles (generic functions) that are needed to achieve these stages. For example, an Initiator is needed to initiate a process. A Planner is needed to plan the process. In some cases, an Observer or a Collaborator is engaged in the process. Thirdly, based on the process, each of these roles mandate certain attributes. For example, in a large project, the Executor of the field construction process has to be a bonded, qualified and licensed actor. The Executor of the design process has to be a qualified, licensed and insured actor. Both of these two executors have liabilities and lines of authority, and are required to meet certain standards and contractual obligations.
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4.4.3 The Actor-Role Semantics
One of the fundamental issues that this research has struggled with relates to the always confusing and fuzzy line between actors and roles, given that the intrinsic and extrinsic attributes lie on a fuzzy continuum. We do not claim that the domain-level ontology has managed to resolve this issue entirely. However, the ontology has attempted to resolve some of the issues involved. It has been the finding of this research that there are two main reasons for most of the overlap (or confusion) between actors and roles, i.e, industrial customs and linguistic ambiguity.
The prevalence of some modelling habits has caused a measure of confusion. For example, a project manager could be seen as an actor in most computer and conceptual models in construction (this has been largely influenced by UML notation, especially lately). Those models see the role(s) of an actor (in this case, the project manger) as the role(s) in computer systems. However, the AR-Onto aims at the role(s) of an actor in the AEC industry. In reality, it is very clear that a project manager is a role in our industry. First, the role is related to a set of functions that have to be performed within the project-management process. Second, the role of “project manager” can be assumed by different entities. For example, a mechanical engineer can be the manager of a project, but so can an architect.
The confusion also relates to linguistic ambiguity. Some confusion has arisen out of naming issues. At certain times, an overlapping concept (the referent) clearly refers to an actor or a role, but in many cases, it is not easy to determine. For example, an accountant could be seen as a role and as an actor. Given that accounting is not a core activity in the construction industry, the role of accounting is very typical and almost standardized in terms of its functions. This role is normally assumed by a person (or a firm) with qualifications in accounting. In other words, the word “accountant” rightfully points to the actor and the typical role of that actor.
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The solution offered by this ontology uses two steps in separating the actor part and the role part of an overlapping concept (see Figure 4-3). The fist step is to separate the logical characteristics of the concept (the referent) into the corresponding actor part and role part. Taking the example of an accountant, one needs to separate the skills of accounting into the actor category and the functions of accounting into the role category. As such, in the AR-Onto actors are skill oriented and roles are function oriented.
The second step is to assign a lexical designation (the reference) to the concept. A set of naming rules has been created to help refer to the logical definition of a referent: - If nothing is attached to the name of a referent, it is assumed that there is no potential confusion. For example, a General Contracting Company is clearly an organizational actor and a Workshop Facilitator is clearly a role. - Actor designation is normally attached as a post-lexical hedge. For actors, the ontology has identified four skill levels for technical actors (professional level, technician/technologies level, skilled labourer level, and non-skilled labourer level) and three skill levels for non-technical actors (professional level, specialist level, and clerk level). A post-lexical hedge is used to indicate an actor at different skill levels. Still using the example of an accountant (a non-professional actor), three possible accountants in the actor category include: Accountant Professional, Accountant Specialist and Accountant Clerk. All those terms refer to a person who has accounting “skills” but at various accreditation levels. - Role designation can use the post-lexical hedge or pre-lexical hedge to incorporate within it different modalities (contexts) depending on where the role is situated. The role of accountant could be designated as Corporate Accountant (equivalent to Account Corporate) or Bookkeeping Accountant. These roles refer to “functions”. - Additional hedges other than major modalities (see Section 4.8) can be added to clarify
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some finer differences. For example, Certified General Accountant Professional indicates not only an accountant at a professional level and/or expertise but also that he/she has attained a CGA certificate.
Figure 4-3: Separating Actors and Roles
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4.4.4 The Actor-Role Mapping
One of the most confusing issues about actor-role modelling relates to the particular role that an actor may assume. In others words, if roles are hats that actors wear, then who should wear what hat(s)? This is very much a dynamic and context-dependent issue. Based on the context, some role-actor associations are very typical, while others are less likely or not even possible. For example, a Structural Engineer could “mostly be” a Designer in a construction project, and “could be” a Constructability Analyst of a design. He/she can be the Construction Manager of a project or the President of a consulting firm. He/she is seldom ever the Art Designer on a project.
In the proposed AR-Onto, this dynamism is modelled mainly through axioms. As shown in Figure 4-4, pictorially, it is represented by solid circles in the case of a “most probable” assignment, an empty circle for a “possible” assignment, and a circle with a cross for a “not probable” assignment. Finally, if no circle is placed at the intersection, then the taxonomy makes no assumption about the relationship. It is left to the user to define the relationship based on the context.
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Figure 4-4: Actor-Role Assignment
Consequently, one can start establishing a more precise profile of actors. This includes his/her typical, possible and not possible roles and engagement in product development, in processes, in projects and in organization (see Figure 4-5). Profiling the actor in this detailed manner, means that we can, for example, direct the right information to this actor based on his or her exact needs and expertise. The ontology has great potential in developing computer applications of semantic information management systems.
STRUCTURAL ENGINEER
Process Project Event
Organization Product Other Modality
Figure 4-5: Outline of Actor Semantic Profile
4.5 AR-ONTO TAXONOMICAL STRUCTURE
A taxonomy is a set of things arranged in supertype-subtype (known as parent-child) hierarchy. In such a subtype-supertype relationship the subtype kind of thing has by definition the same constraints as the supertype kind of thing plus one or more additional constraints. In other words, a subtype concept is a kind of supertype concept. For example, Car is a subtype of Vehicle. So any car is also a vehicle, but not every vehicle is a car.
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Therefore, a thing needs to satisfy more constraints to be a car than to be a vehicle. AR-Onto has developed taxonomies for four major construction entities: Actor, Role, Actor Attribute, and Role Attribute.
4.5.1 Taxonomy for Actors
The concepts encapsulated in the taxonomy of actors have been distilled from a broad review of literatures including construction literatures, existing occupation classification systems and industry classification systems. Actors have been grouped into three categories: - Individual Actor: An individual actor is a human being who performs in the construction industry. - Organizational Actor: An organizational actor is an established social arrangement where people work together collectively in the AEC industry. - Other Actor: Most actors can be arranged into either the class of individual actors or organizational actors, but there are still some exceptions. For example, in transportation- operation modelling, sometimes the actor is a driver-vehicle pair. The class Other Actor is used to cover the concepts not belonging to either the Individual Actor class or the Organizational Actor class. The proposed AR-Onto will mainly cover the Individual Actor class and the Organizational Actor class, because there are no significant concepts in the AEC industry belonging to the Other Actor class.
4.5.1.1 Individual Actor As has been discussed before, actors are mainly defined by the innate attributes of construction entities, and as such, most individual actors are identified by their skills. This research took The National Occupational Classification (NOC 2006) as its primary point of reference in defining individual actors. It is the nationally accepted reference on occupations
Chapter 4: Domain Ontology for Actors and Roles 93 in Canada and includes 520 occupational group descriptions throughout Canada’s labour market. About 80% of the job titles listed in the NOC are classified as actors, while others are classified as roles based on the methodology employed in this research. Some other reference sources used in developing this taxonomy of individual actors have included: - The Career Handbook (Second Edition). The Career Handbook provides global ratings assigned to occupations in the Canadian labour market to further define skills, worker characteristics and other indicators related to occupations that are important for career exploration and informed career decision-making. It includes information on aptitudes, interests, involvement with data/people/things, physical activities, environmental conditions, education/training indicators, career progression and work settings. - Standard Occupational Classification (SOC 2000). Similar to the NOC in Canada, the SOC system is used by federal statistical agencies in the United States to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of over 820 occupations according to their occupational definition. To facilitate classification, occupations are combined to form 23 major groups, 96 minor groups, and 449 broad occupations. Each broad occupation includes detailed occupation(s) requiring similar job duties, skills, education, or experience. - The Occupational Outlook Handbook (OOH 2008) The Occupational Outlook Handbook is a publication of the United States Department of Labor’s Bureau of Labor Statistics that includes information about the nature of work, working conditions, training and education, earnings, and job outlooks for hundreds of different occupations. The Handbook is released biennially with its companion publication, the Career Guide to Industries. The current 2008-2009 edition was released in December 2007 and includes employment projections for the period 2006–2016. - The Dictionary of Occupational Titles (DOT 1991). The Dictionary of Occupational Titles was the creation of the U.S. Employment Service, which used its thousands of
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occupational definitions to match job seekers to jobs from 1939 to the late 1990s. However, in a 1980 study, the National Research Council reviewed the DOT and the job analysis methodology used to create it, and concluded that it was not based on what was then current vocational theory. The DOT is still a good resource for finding job descriptions although it was abandoned and replaced by O*NET. - Occupational Information Network (O*NET). The O*NET program is the nation’s primary source of occupational information. Central to the project is the O*NET database, containing information on hundreds of standardized and occupation-specific descriptors. The database, which is available to the public at no cost, is continually updated by surveying a broad range of workers from each occupation. Information from this database forms the heart of O*NET OnLine, an interactive application for exploring and searching occupations. - Various textbooks in the construction domain. Those literatures specifically developed for the construction industry include some actor concepts that are too specific to be included in the general occupational systems.
The NOC groups all job titles into a matrix, using skill level as one dimension and skill type as the other dimension. Skill level is defined generally as the amount and type of education and training required to enter and perform the duties of an occupation. Four skill-level categories are identified in the NOC: university degree (bachelor’s, master’s or doctorate), community college or equivalent education (2-3 years of college studies, 2-5 years of apprenticeship training, or 2-3 years of secondary school plus more than 2 years of on-the-job training), secondary school or equivalent education (1-4 years of secondary school education or more than 2 years of on-the-job training), and no formal education (or just short on-the-job training). When it comes to the management occupations listed, the NOC does not assign a skill-level category to them, because there are many factors other than education and training to be considered in hiring for management positions and management people have a broad
Chapter 4: Domain Ontology for Actors and Roles 95 range of educational backgrounds. Skill type is defined as the type of work performed. The NOC recognizes the following skill types: - Business, finance and administration - Natural and applied sciences and related occupations - Health occupations - Occupations in social science, education, government service and religion - Occupations in art, culture, recreation and sport - Sales and service - Trades, transport and equipment operators and related occupations - Occupations unique to primary industry - Occupations unique to processing, manufacturing and utilities
The AR-Onto’s taxonomy for individual actors has adopted the NOC’s structure, and uses skill level as its first level of subclasses but using slightly different grouping criteria. The four highest-level subclasses of individual actors are: - Professional Individual. This class represents individual actors who have been formally educated and trained to perform creative and intellectually challenging work. This class corresponds to the Level A (university degree) category in the NOC. - Technician/Technologist. This class represents individual actors who have finished the education and training needed to perform skilled work. This class corresponds to the Level B (college education) category in the NOC. - Skilled Labour. This class represents individual actors who need limited education and training to perform their work. This class corresponds to the Level C (secondary school education) category in the NOC. - Non-Skilled Labour. This class represents individual actors who do not need formal education but only to be able to perform general labour. This class corresponds to the Level D (no formal education) category in the NOC
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In each of four top categories of individual actors, concepts are grouped by skill types. All nine skill types defined by the NOC are examined to distill proper concepts for the construction industry. While most of the NOC occupation titles are classified as actors based on the discussion in Section 4.4, some of them are identified as role (for example, most titles under the management occupation category). AR-Onto did not include all actor concepts, because the NOC covers all job titles in the Canadian labour market and the scope of AR-Onto mainly concerns actors related to the construction industry. Three levels of coverage are employed: - The core concepts closely related to the construction industry are fully covered. For example, there are 87 subclasses in the class of Heavy Equipment Operator to cover almost all actors in this field. - The most important supporting concepts are covered. Those concepts are important to support this industry and should not be ignored. For example, Accountant in financial service and Bus Driver in transit service. - Concepts not related to the construction industry are not covered. However, the higher level concepts in those NOC major groups are still listed in the taxonomy for two reasons: (1) other taxonomies using the same NOC as a primary reference can easily be coordinated with this taxonomy, and (2) this taxonomy can easily be extended in the future to fit more application requirements.
As the primary reference for individual actors in developing AR-Onto, the NOC does not provide in-depth coverage for all actor concepts in the construction industry, because it has to balance the requirement of covering all occupations with reasonable amounts of detail. For example, the NOC has five types of carpenters listed in group 7271 and in general describes what carpenters do and who hires carpenters. However, as a specialty trade in construction, there are some types of carpenters that are not completely covered by the NOC such as, for
Chapter 4: Domain Ontology for Actors and Roles 97 example a Trim Carpenter, who specializes in the molding and trim involved in door and window casings, mantels, baseboards, and other types of ornamental work. As for the gap between the description of general occupational classification systems and the occupational specialties of the construction industry, it is filled by those textbooks and handbooks that cover the concepts that belong specifically to the construction industry.
Figure 4-6 shows the first two levels of the actor’s taxonomy. In total, there are about 990 concepts in the class of Individual Actor. Appendix F.1 provides the description of major individual actors at higher levels in the taxonomy.
Figure 4-6: Actor’s Taxonomy
4.5.1.2 Organizational Actor In the domain of construction knowledge, many actors are not an individual person but an
Chapter 4: Domain Ontology for Actors and Roles 98 organization such as, for example, an Architectural Design Firm who could play the role of Designer in a construction project or the role of the Initiator of an architectural design process. The AR-Onto divides all organizations into two categories: Government Organizations and Non-Government Organizations.
The primary reference for the taxonomy of organizational actors in the AR-Onto is the Canadian version of The North American Industry Classification System (NAICS 2007). The NAICS is used by business and government to classify and measure economic activity in Canada, Mexico and the United States. It was developed jointly by the U.S. Economic Classification Policy Committee (ECPC), Statistics Canada, and Mexico's Instituto Nacional de Estadistica, Geografia e Informatica, to allow for a high level of comparability in business statistics among the North American countries. The NAICS groups all business establishments into twenty major sectors based on their business activities.
Government organizations in the AR-Onto refers to governing authorities for public administration. This class corresponds to Sector 92 Public Administration in the NAICS. The proposed AR-Onto is aimed at the construction industry in general and the Canadian market more specifically. Thus, the classification of organizational actors uses the level of government organizations as the criterion of the first subclass: International Government Organization, Federal Government Organization, Provincial and Territorial Government Organization, Local Municipal and Regional Government Organization, and Canada’s Aboriginal Government Organization. The reason that administration levels are at the top priority is that, in the construction industry, the administration of and authorization of many projects are related to government offices at the corresponding levels. Within each level, governmental organizations are grouped into subsectors as is done in the NAICS. Some subsectors include: The Economic Services Organization, The Citizenship and Immigration Services Organization, The Defence Services Organization, The Financial Services
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Organization, The Human Resource Services Organization, The Executive Services Organization, The Legislation Services Organization, The Protective Services Organization, etc. Figure 4-7 shows the first two levels of governmental organizations.
Figure 4-7: Government Organization’s Taxonomy
Non-governmental organizations in the AR-Onto are classified according to sectors set by the
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NAICS. Similar to the coverage of individual actors, the AR-Onto completely covers organizational actors that are closely related to the construction industry, partially covers organizational actors that support this industry, and does not cover organizations that are not related to construction (but does list the highest level to be the interface for collaboration with other ontologies and extension for different applications). There are about 440 organizational actors included in the proposed AR-Onto. Appendix F.2 provides the description of higher-level organizational actors.
4.5.2 Taxonomy for Roles
Figure 4-8: Major Contexts of Roles
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Actors in the construction industry have relatively stable identities but dynamic features when situated in different contexts. This is the fundamental reason that the AR-Onto differentiates between actors and roles. For example, in a design-bid-build context, an engineering firm normally assumes the role of a designer (making and certifying designs). In a deign-build context, an engineering firm could assume the role of project manager. During project design or construction, engineering firms could be involved as decision makers, analysts, or advisors. Thus, the assertion “engineering firm has the role of designers” does not hold in all contexts.
All role concepts are first classified by contexts in the taxonomy. Some important contexts include: Role of Process, Role of Event, Role of Project, Role of Product, and Role of Organization. There is a parallel class called Role of Other Context to cover some occasional contexts such as Role of Employment, Role of Legislation, or Role of Program. Based on different context, each class includes a set of subclasses representing different roles in the given context. Figure 4-8 shows the major contexts and the major roles in the context of project. Appendix F.3 provides the description of major roles.
4.5.3 Taxonomy for Attributes
An attribute is an abstraction or description of a characteristic or a property of an entity. It reflects an essential feature of the entity and helps to identify and distinguish it from other entities. Sometimes people are confused between two similar concepts: attribute and property. In this research, the two terms are used interchangeably as in modern philosophy and logic. They both describe the quality of an entity. Attributes generally cover all qualities or characteristics, while properties tend to be qualities or characteristics having determinable values such as, for example, Age or Communication Language.
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Attributes of actors and roles are modelled as classes in the AR-Onto. From the ontological model described in section 4.3, one can see that “attribute” is a very important concept in modelling domain knowledge. All concepts including entities and systems have attributes. Because the objective of the proposed ontology is to model actors and their roles in the construction industry, and because attribute is an important construct for the purpose of modelling, embedding attributes into ontology relationships conflicts with the dynamic nature of role playing. In addition, the proposed AR-Onto is not intended to model all actors and roles exhaustively, but rather and more importantly to provide a framework for any modelling extension in the construction industry. It is therefore necessary to model attributes as classes.
Another reason to model attributes as classes is to understand the taxonomical relationships between attributes and to support semantic searches about attributes in applications. For example, Experience is an attribute for both actors and roles. By modelling all experiences with an explicit “is-a” relationship, the ontology can assert that Field Construction Experience is a kind of Experience, and that On Site Equipment Operation Experience is a kind of Field Construction Experience. Later, in an application, if someone (a user) is searching for a person who has field construction experience but the profile of a given actor in the system claims to have some on site equipment-operation experience, then the system can return this information to the user based on the semantics of Experience.
4.5.3.1 Source of Attributes There are two types of references in developing the attribute taxonomy: content model of The Occupational Information Network (O*NET) and domain literatures. O*NET is the primary source of occupational information for the United States. O*NET, sponsored by the Employment and Training Administration of the United States Department of Labor, was developed by the National Center for O*NET Development. O*NET was created to replace
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The Dictionary of Occupational Titles (DOT), which was considered obsolete and inefficient during the early 1990s and abandoned by the Department of Labor in 1998 (Byars and Rue, 2006).
The content model is the conceptual foundation of O*NET. It provides a framework that identifies the most important types of information about a variety of works and integrates them into a theoretically and empirically sound system. The model embodies a view that reflects the character of occupations (via job-oriented descriptors) and people (via worker-oriented descriptors). The content model also allows occupational information to be applied across jobs, sectors, or industries (cross-occupational descriptors) and within occupations (occupational-specific descriptors). These descriptors are organized into six major domains, that enable the user to focus on areas of information that specify the key attributes and characteristics of workers and occupations (The National Centre for O*NET Development, 2008). Three of those six domains are related to workers and are suitable for distilling attributes for actors: - Worker Characteristics. These are enduring characteristics that may influence both work performance and the capacity to acquire the knowledge and skills required for effective work performance. - Worker Requirements. These are descriptors referring to work-related attributes acquired and/or developed through experience and education. - Experience Requirements. These are requirements related to previous work activities and are explicitly linked to certain types of work activities.
Besides the content model of O*NET, a broad range of domain literatures, for example (Meredith and Mantel, 2006) and (Chartered Institute of Building, 2002), have been reviewed to collect more domain specific attributes for construction actors and roles.
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4.5.3.2 Attributes for Actors and Roles There are two kinds of attributes for actors: Physical Attribute and Abstract Attribute. Physical attributes are concrete attributes of actors whose values are physically measurable or clearly determinate. Abstract attributes are qualities of actors which are not easy to measure or quantify, for example Experience or Skill. Some attributes are applicable for both individual and organizational actors, for example Name. Other attributes are specific for either individual actors or organizational actors. For example, Educational Background is not an attribute of organizational actors and Bonding Capacity is not an attribute of individual actors. Figure 4-9 and 4-10 show taxonomy structure of actor attributes. Attributes for roles are mainly those related to behaviours such as Liability, Responsibility, Right, Authority, etc. Appendix F.4 provides descriptions of higher-level attributes.
Figure 4-9: Attributes of Individual Actors
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Figure 4-10: Attributes of Organizational Actors
4.6 AR-ONTO RELATIONSHIPS
In an ontology, relationships link concepts to enrich the semantics, i.e., to clarify the context of a given domain of subject. From the perspective of relational theory, an object (a concept in this research) is only meaningful when relative to other objects (concepts). People get to know the reality or a physical system by understanding the objects and their relationships. Because the ontology aims to model the domain knowledge of the construction industry, relationships remain a high priority throughout ontology development.
4.6.1 Major Types of Relationships
Sixteen generic relationships have been identified in the RNA method. They are organized in the relationship taxonomy shown in Figure 4-11 with the following definitions (Yoo and Bieber, 2000):
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Generalization/ Specialization Characteristic Self Descriptive Internal Occurrence Relationship Configuration/ Whole-Part/ Aggregation Composition Membership/ Grouping Generic Classification/ Relationship Instantiation Equivalence Comparison Similar / Dissimilar External Ordering Relationship Activity Association/ Influence Dependency International Socio-Organizational Temporal
Spatial Figure 4-11: RNA’s Generic Relationships, adapted from (Yoo and Bieber, 2000)
- Generalization relationship: connects an item of interest to the items whose concepts include its concept in a taxonomy. - Characteristic relationship: connects an item of interest to its attributes, parameters, metadata and other background information. - Descriptive relationship: connects an item of interest to definitions, illustrations, explanations, and other descriptive information. - Occurrence relationship: connects multiple instances/views/uses/transformations of the same object in different parts of a system. - Configuration/Aggregation relationship: connects a part to other parts or a whole
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functionally or structurally. - Membership/Grouping relationship: connects a member of a collection to other members or a whole collection. - Classification relationship: connects an item of interest to its instance or class. - Equivalence relationship: connects instances of the exact same object to a given item (i.e., same copies of a book or an exact match in information retrieval). - Similar/Dissimilar relationship: connects all items that share some positive or negative degree of similarity. - Ordering relationship: puts items in some kind of sequence. - Activity relationship: deals with relationships that exist among elements that are involved in some kind of activity (e.g., input, tools, and output). - Influence relationship: connects an item of interest to the item over which it has some kind of influence (i.e., a causal or control relationship) - Intentional relationship: connects an item of interest to the goals, arguments, issues, decisions, opinions, and comments associated with the item. - Socio-organizational relationship: connects an item of interest to the position, authority, alliance, role, and communication associated with the item in a social setting or organizational structure. - Temporal relationship: connects an item of interest to temporally related items. - Spatial relationship: connects an item of interest to related items in spatial dimensions.
For each of the sixteen relationships, there is a set of possible questions which an analyst could ask himself/herself about each concept identified in the taxonomy. The answers to these questions will derive relationships within a specific category. For example, in the category of Activity relationships, possible questions could be: What are this entity’s inputs and outputs? What resources and mechanisms are required to execute this entity? In order to generate enough questions for each generic category, relationship analysis needs to provide a
Chapter 4: Domain Ontology for Actors and Roles 108 meaningful set of domain-independent categories to further specify the nature of each generic relationship (Yoo and Bieber, 2000). An example is the domain-independent categories of Activity relationship, which are groups of relationships that represent related behaviours or functions associated with a specific concept, such as production, creation, examination, execution, etc.
As the methodology being discussed here aims at the development of a domain-level ontology, it is necessary to take a further step in relationship analysis and raise it to the domain-specific level. As soon as a set of domain-specific terms map to the domain-independent level, relationship analysis is driven to the domain-specific level. Still using the example of Activity relationships as an illustration, the terms “design” and “construction” are specific terms in the AEC industry to represent the production (a domain-independent term) of a set of drawings or a physical facility respectively.
The AR-Onto recognizes three types of relationships: a hyponymy relationship (is-a relationship), a meronymy relationship (whole-part relationship) and a cross-tree relationship (behavioural or descriptive relationship). The schema of relationships follows the framework set by (El-Diraby, 2009).
Hyponymy Relationships Hyponymy relationships mainly represent superclass-subclass relations in a taxonomy tree, and as such they are also called taxonomical relationships or “is-a” relationships. For example, a Structural Engineer is a Civil Engineer which is an Engineer. This kind of relationship is the backbone of building taxonomies. From an object-oriented point of view, a subclass inherits all attributes of its superclass and possesses more specific attributes than its superclass.
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There are some variations on “is-a” relationships as defined by (El-Diraby, 2009). Some relationships are used to deal with synonymy concepts (for example, “is same as” or “is equivalent to”) or similar concepts (for example, “is like” or “is similar to”). Some other relationships deal with antonymy concepts (for example, “is opposite to” or “is negative to”) or disjoint concepts (for example, “is disjointed with”).
Meronymy Relationships Meronymy relationships describe whole-part relations between two concepts, i.e. one concept is a part of the other. For example, a First Name is a part of a Full Name. Hyponymy relationships decompose concepts into subclasses based on similarity, while meronymy relationships decompose concepts into parts based on feature comparison. Subclasses inherit features from the superclass but parts do not inherit features from the whole, though there is an upward inheritance for some attributes such as colour, material, and function (Tversky, 1990). Pribbenow (2002) concludes that hyponymy relationships exist within concepts, while meronymy relationships exist between concepts.
Cross-Tree Relationships Cross-tress relationships are mainly used to represent behavioural or descriptive relationships between concepts which are not the same type. For example, an Architect plays the role of Architectural Designer, which is a subclass of Designer. In this example, Architectural Designer is a type of Designer but does not have a taxonomical relationship with the concept Architect. This kind of non-taxonomical relationship enriches the semantics of a body of domain knowledge, because (1) the non-hierarchal links between concepts further define the essence of a given concept other than its type, and (2) they can model the behaviours of concepts. As such, cross-tree relationships are able to model tacit knowledge (at least partially) of a given domain by clarifying their relative position to other concepts.
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4.6.2 Schema of Relationships
Relationships developed in the AR-Onto follow the schema proposed by (El-Diraby, 2009) where each higher-level relation represents a taxonomy of relationships. Hyponymy and meronymy relationships are pretty much onefold, while cross-tree relationships are multifold and could be categorized in groups based on different perspectives. The major groups include attributive relationships, causal relationships, functional relationships, provenience relationships, conformance relationships, spatial relationships, temporal relationships, etc.
Many relationships between concepts in the construction industry possess a dynamic aspect. Fuzzy representation of relationships is employed to accommodate this feature. The AR-Onto recognizes two kinds of dynamics in relationships. The first situation is the uncertainty of relationships. Some relationships are always true. For example, “Budget is always a Project Objective.” Some relations are normally true. For example, “a Technical Designer normally has an interest in a Design Code”. Some relations could be true, such as, for example, “an Electrical Engineer could play a role of Technical Designer.” This kind of uncertainty is caused by different contexts or settings. In the proposed AR-Onto, this kind of dynamics is dealt with by means of a fuzzy representation similar to that elaborated on for mapping actor and roles (see Section 4.4.4). Here the fuzzy representation is extended from an “is-a” relationship to any possible non-taxonomical relationship. In this way, any given relationship could have several variations associated with different levels of possibility such as “always”, “normally”, “could” or “never”.
Another kind of dynamics is the different “flavours” of a given relationship. A more generalized relationship could be fine-tuned to allow for different flavours. For example, an Architect “is involved” in a Design Process, but this involvement could include several more specific functions/relationships: - The Architect “manages” the Design Process.
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- The Architect “executes/conducts” the Design Process. - The Architect “coordinates” the Design Process. - The Architect “initiates” the Design Process. - The Architect “observes” the Design Process. The proposed AR-Onto has organized relationships with such generalized-specific relations into a relationship taxonomy because it has been recognized that the more specific relationships are parts of a more generalized relationship. In the above-mentioned example, “management”, “execution”, “coordination”, “initiation”, and “observation” are all kinds of “involvement”. Appendix F.5 provides descriptions of the major relationships included in the AR-Onto.
4.7 AR-ONTO AXIOMS
A domain ontology is not a universal model of the body of knowledge in a given domain, but aims at representing domain knowledge, or in other words, provides the basic constructs and creates models for the domain. Here a model means a mathematical construct that, with the addition of certain verbal interpretations, describes observed phenomena (von Neumann, 1961). El-Diraby (2009) indicates that, in the creation of an ontological model of a domain, axioms have a far-reaching impact on the behaviour of concepts and could significantly limit the extendibility of a domain ontology. This is more understandable in a domain such as construction, which has subjective rules.
As a domain ontology, the AR-Onto has adopted a minimalist approach in dealing with axioms, where only the axioms which are relevant to domain-level knowledge are included. This approach provides a way to maintain the validity of a domain ontology, while at the same time granting the maximum flexibility to application-level ontologies in formulating
Chapter 4: Domain Ontology for Actors and Roles 112 their own axioms. The axioms formulated in the AR-Onto have two main objectives: - Protect the integrity and consistency of the ontology, and - Restrict/Constrain the behaviours of concepts.
There are three types of axioms used in the AR-Onto to constrain entities. Appendix F.6 provides descriptions of major axioms. - Cardinality Axioms. Cardinality axioms are used to restrict the number of elements of a proposition. They are mainly attributive axioms. Attributive axioms are used to restrict attributes of actors and roles. Some attributive axioms concern the cardinality of the attributes, while others concern the possession of attributes (descriptive axioms). - Descriptive Axioms. Descriptive axioms are used to constrain the attributes of actors and roles, and as such, they are attributive axioms as well. - Behavioural Axioms. Behavioural axioms are used to restrict functions and behaviours of actors and roles.
The AR-Onto recognizes two levels of axioms. The first level consists of the axioms and rules that are domain independent. For example, “any Actor has a Name” or “any Role is played by an Actor”. The second level consists of the axioms and rules that are domain specific, i.e., those axioms and rules that are specifically valid for the AEC industry regardless of their application contexts inside the industry. Examples are “A Certified Accountant owns a Professional License” and “an Owner awards a Contract”.
4.8 AR-ONTO MODALITIES
As has been discussed in Sections 4.2 and 4.3, the concept Modality is employed to accommodate the multidimensional feature of a subjective domain such as construction in
Chapter 4: Domain Ontology for Actors and Roles 113 domain knowledge modelling. The AR-Onto has organized the structure of concept taxonomies based on their basic types, which are the most intuitive or commonly accepted by the industry. This is the basic modalities of concepts. Later, a series of modalities are identified based on a variety of perspectives. Each of those modalities has one or more specific features (attributes) to be bound together with the basic modality.
The taxonomy of individual actors in the AR-Onto is organized first by skill levels and then by skill types. The two basic modalities are Skill Level Modality and Skill Type Modality. Other modalities for individual actors could be: - Domain Modality. Each actor belongs to a technical domain based on his/her knowledge, for example Engineering Domain, Economic Domain, Architectural Domain, etc. Individual actors could be grouped by different subject domains. - Licence Modality. Many individual actors in the construction industry need to have a certain licence to perform their job such as, for example, the Certificate of Professional Engineer or the Certificate of Licensed Electrician. Licence could be a modality for grouping individual actors. - Industry Modality. Each actor belongs to a specific industry. Industry Modality is not a redundant or in conflict with Domain Modality – two actors could belong to a same domain but different industries. For example, an Architect and a university Professor who teaches architecture both belong to the architectural domain, but the Architect belongs to the construction industry, while the Professor belongs to the education industry.
The two basic modalities embedded in the organizational actor’s taxonomy are Government Modality and Industry Modality – all organizations are first grouped according to whether they are government organizations or non-government organizations, and then according to industry sectors for non-government organizations. Other modalities for organizational actors could be:
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- Profitability Modality. Some organizations are for-profit organizations, while others could be non-profit organizations. People normally tend to recognize non-government organizations as non-profit organizations and call all for-profit organizations enterprises. However, some enterprises owned or operated by government (crown corporations) are running for profit, or at least to cover their operating costs. Profitability, then, should be a separate modality for organizations. - Constitutional Modality. This modality is specific to government organizations. A government organization is a legislative, judiciary, or executive organization. - Licence Modality. Some organizations (for example some specialty trade contracting companies) need to obtain special licences to conduct business in a certain field.
The basic modality for roles is Context Modality, which means that the actors are first grouped according to the subject contexts. The contexts could be projects, processes, events, products, programs, organizations, employment, resources, etc. Some other modalities for roles could be: - Seniority Modality. Roles are classified as basic and advanced based on the organizational seniority of individuals. For example, Urban Planner is a role in AR-Onto. This represents the basic or generic level of seniority. A Chief Urban Planner is more senior. - Span of Control Modality. Roles can be dedicated to projects (project role) or corporate level (corporate role). For example, Project Estimator is a role at the project level. Corporate Estimator is a role at the corporate level. This can be further classified into other levels: local, regional, national and international categories, for example, National Marketing Manager. - Speciality Modality. Within a certain domain, roles can be subdivided into smaller specialties. For example, one can talk of a Tax Accountant or Management Accountant, i.e., an accountant that specializes in taxes or corporate account management
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- External Role Modality. This modality clusters all roles external to a given project, process, event, organization, and so on. - Internal Role Modality. This modality clusters all roles internal to a give project, process, event, organization, and so on. - Life Cycle Role Modality. This modality clusters all roles relevant to the life cycle of a given project or process, for example Project Initiator or Process Controller. - Managerial Role Modality. This modality clusters all managerial roles, for example Project Manager, Chairperson of an organization, or a company’s President. - Technical Role Modality. This modality clusters all roles for technical works, for example Project Designer or Product Analyst.
For the attributes of actors and roles, the two basic modalities are Human Attribute Modality and Organization Attribute Modality. The former is used to collect all human being related attributes and the latter is used to collect all organization related attributes. In each category, another two modalities are employed: Abstract Attribute Modality and Physical Attribute Modality. Physical attributes are concrete attributes of actors and roles whose values are physically measurable or clearly determinate. Abstract attributes are those qualities of actors and roles that are not easy to measure or quantify. Other modalities of attribute could be: - Mutability Modality. Some attributes are not changeable (stable over a relatively long period), for example Name, Language, Experience, etc., while some other attributes are changeable such as, for example, Age, Address, Certificate and Licence, etc. - Expression Modality. Some attributes are quantitative, for example Age and Gender, while others are only qualitative, for example Liability and Responsibility.
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4.9 AR-ONTO CODING
Most popular ontology editors have been reviewed in Chapter 2 and Protégé-OWL (version 3.4) has been selected to formally encode the proposed AR-Onto. The coding has included the implementation of taxonomies, relationships and axiom. Taxonomies are implemented as class hierarchical structures in the Protégé-OWL. Figure 4-12 shows a partial view of concept taxonomies. For each concept, some annotation properties provided by Protégé-OWL have been used to annotate the concepts. The Doblin core ontology property “dc:source” is used to document the source of the definition for each concept. For example, in Figure 4-12, the definition of Civil Engineer is from NOC 2006. The RDFS property “rdfs:comment” is used to document the definition of concepts (see Figure 4-12).
Figure 4-12: Taxonomy Implementation
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The relationships are implemented in Protégé-OWL as properties. Some attributes qualify as object properties which use classes as range. For example, the domain of the property “play” is class Actor and the range of the property is class Role. Some attributes qualify as datatype properties which use data as range. The data could be any type including numbers, strings, date, etc. For example, the domain of the property “has age” is class Actor and the range of the property is a datatype Integer. Figure 4-13 shows the partial view of object properties and datatype properties.
Figure 4-13: Relationship Implementation
Axioms are implemented in Protégé-OWL through the use of “existential property restrictions” and “necessary and sufficient conditions.” Existential property restrictions can
Chapter 4: Domain Ontology for Actors and Roles 118 define the domain, range, and cardinality of a property. For example, an Individual Actor has exactly one Family Name, Given Name, Age, and Gender (Figure 4-14). Necessary and sufficient conditions can be used to define the taxonomical relationships and equivalent concepts. If a necessary condition is satisfied, it means that a superclass-subclass relationship has been established. For example, in Figure 4-14, an Individual Actor is necessary to be an Actor. If a necessary and sufficient condition is satisfied, it means two classes are equivalent.
Figure 4-14: Axiom Implementation
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5 APPLICATION ONTOLOGY FOR CIKP 5
pplication ontologies are ontologies specially developed for certain application A purposes. They are application-dependent (van Heijst et al., 1997). Unlike the domain ontologies which encapsulate the knowledge of a given subject domain, an application ontology is used to model the knowledge required for a particular application. This goal is normally achieved by extending or specializing a domain ontology. If the application involves more than one domain, then it is necessary to combine two or more extended or specialized ontologies from several domains.
This Chapter presents an application ontology designed to facilitate information exchange and knowledge sharing in the construction industry. This application ontology will serve as a cornerstone for the development of the Construction Information and Knowledge
119 120
Protocol/Portal, and thus, it is named CIKP-Onto. It has utilized several domain-level ontologies in the AEC industry and has incorporated within it existent knowledge of communication: - The Actor-Role ontology (AR-Onto) is specialized to model the actors and roles in the construction industry with respect to their need for information and knowledge. - Two domain-level ontologies in particular have been incorporated and extended to develop this information and knowledge portal. One is the domain-level ontology in modelling products in the construction industry, and another is for the processes in this industry. The concepts, relationships and axioms in these two ontologies that are required for building the CIKP-Onto have been included and further extended to meet the requirements of the proposed application. - An upper-level ontology (DOCK) proposed by (El-Diraby, 2009) has been extended to cover some important domain concepts such as Project. - Some vocabularies, relationships, and rules used in communication have been included to model processes of information exchange and knowledge sharing.
5.1 MECHANISM OF INFORMATION AND KNOWLEDGE COMMUNICATION
It is necessary to understand the process of technical communication before developing the CIKP-Onto to encapsulate the knowledge of communication. The discussion reviewed in Section 2.2, makes it clear that the effective technical communication of information and knowledge is of vital importance for any organization wishing to move towards the status of a learning organization through knowledge management. It has also shown that the linear communication model still remains the fundamental approach in many industries including construction. Although a many-to-many communication paradigm is encouraged in this research, the study of the linear communication model is still valid because the
Chapter 5: Application Ontology for CIKP 121 many-to-many communication paradigm is composed of many linear communication links and each link connects two actors as defined by the AR-Onto.
In the basic linear communication model, a message is sent by an information source (an actor) via a communication channel to a destination (another actor). Berlo (1960) summarized the following elements of a communication process: a communication source; an encoder, a message; a channel; a decoder; and a communication receiver. In this research, the communication model is further extended to include three more concepts: attribute, communication mechanism, and communication constraint. Figure 5-1 shows the basic model. In this model every element of information and knowledge communication has its own attributes and the information and exchange communication is subject to some constraints and controlled by certain mechanisms. The Knowledge Item is central to the information and knowledge communication model. A Knowledge Item, as the message in traditional linear communication model, is sent by a source which is an Actor in this research, and is received by a destination which is another Actor. A communication channel connects both the source and the destination. The Knowledge Item is composited by a composer (an encoder) and is viewed by a viewer (a decoder).
Knowledge Item A knowledge item is the thing to be communicated. Berlo’s (1960) theory focused on information science, applying it strictly to the communication of a textual message. The model presented in this research tries to provide information communication with a more general scope. The thing to be communicated can be anything that carries information, ranging from a hard copy of some documents to a computer file. According to the scope of CIKP, a knowledge item is a digitalized modality that can be transferred via the Internet. Section 5.3 will discuss the taxonomy of a Knowledge Item in further detail.
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Figure 5-1: Information and Knowledge Communication Model
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Source and Destination A source is the initiator of information communication. It is also called a sender and has a few other alternative names in different contexts, for example, in publish/subscribe systems where it is normally called a producer or a publisher. A destination is the receiver of the information. Similarly in publish/subscribe systems this is called a consumer or a subscriber. Berlo’s (1960) model has been extended to have three kinds of sources or destinations: - Individual: A person to send or receive a Knowledge Item. - Organization: Sometimes a Knowledge Item is coming out of (or going to) an organization if it is an act of duty. - Information System: As more and more intelligent information systems are being used, one can visualize some information being sent out or received by automated systems such as a sensor or a bar code reader. Individual and Organization are two subclasses of Actor in the AR-Onto.
Communication Channel A communication channel is the method by which the Knowledge Item is communicated. This class represents the delivery methods of information items, including - emailing to deliver texts or computer files, - postal mailing to deliver hard copy of information items, - faxing to deliver texts and images, - personal communication such as phone calls or face-to-face meetings for delivering messages, - web presenting to deliver online contents such as web pages, - automated information communication to deliver computer files or signals.
Composer As the subject of the communication model has been extended from a message to a
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Knowledge Item, the element “encoder” in Berlo’s (1960) model is no longer valid in this model because an encoder is mainly used for a message. The concept of composer is proposed to handle the composition of a Knowledge Item. Some composers frequently used in communication information and knowledge include: - printing devices for hard copy of documents, - audio/video recording devices for multimedia contents, - fax devices for materials to be faxed, - composition software such as word processing software, CAD software, image processing software, audio/video processing software, programming software, email composer, Web page/site editor, etc. - human beings for composing languages.
Viewer Similarly the concept of “encoder”, the element “decoder” in Berlo’s model is replaced in this communication model by a new concept, “viewer”, an entitiy which will be responsible for presenting a Knowledge Item. Some viewers in information and knowledge communication include: - viewing software including document viewer, video player, email browser, web browser, image viewer, etc. - fax devices for faxed materials, - human beings for oral communication or other possible modes of communication.
Attribute Attributes are used to describe features of information and knowledge communication. This is also the building block for defining an event in publish/subscribe systems. In this communication model, the attributes of three elements are highlighted: attributes of the source, attributes of the destination, and attributes of the knowledge item. Section 5.3 will
Chapter 5: Application Ontology for CIKP 125 discuss those attributes in more details.
Constraint Three types of constraints are acknowledged in the information and knowledge communication model: - Communication Channel Capacity: The maximum volume of information that can be handled by a certain communication channel. - Communication Cost: Money, time, or network resource to be consumed by the information communication process. - Communication Channel Speed: The speed at which the information item can be delivered.
Mechanism Mechanism in this model refers to the method that controls communication. Some examples include the communication methods that establish communication channels, the composition methods that determine the types of composers, and the viewing methods that determine the types of viewers.
5.2 REQUIREMENT ANALYSIS AND COMPETENCY QUESTIONS FOR CIKP-ONTO
Requirement analysis is the key beginning step for developing any ontology, and this holds true for the CIKP-Onto. As this ontology will be used to empower the information system proposed to facilitate information exchange and knowledge sharing in the construction industry, it contains the following requirements: - The ontology needs to represent most actors and roles in the construction industry that have a strong desire for information and knowledge.
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- The ontology needs to be able to profile those actors and roles in terms of their own attributes and the attributes of relevant entities. - The ontology needs to cover all major concepts (not only Actor, Role, and their Attribute) required to profile actors and their roles. - The ontology needs to include all necessary relationships to describe the functions and behaviours of actors and roles. - The ontology needs to be able to represent the basic needs of major actors and roles for information and knowledge - The ontology needs to include all necessary axioms to perform adequate inference about the information and knowledge needs of actors and roles.
The application scenarios defined for the domain-level ontology AR-Onto have now been refined for the application-level ontology CIKP-Onto and based on that, a series of competency questions have been gathered at the application level to (1) distil concepts, relationships and axioms for developing the CIKP-Onto and (2) validate the CIKP-Onto when it has been formally formulated. - Anyone in the construction industry should be able to share information about any project. The shared information can be annotated in many ways from different perspectives, which will allow for greater possibilities for describing the information as fully as possible. The ontology should be able to know who needs this shared project information. - People can share generic knowledge about the AEC industry. Similar to project information, the shared knowledge can be flexibly described and the ontology can infer the needs of this shared knowledge. - People can propose new virtual Communities of Practice (CoPs) or join/leave existing communities. The ontology should be able to suggest appropriate communities to actors and roles.
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5.2.1 Concept Questions
Table 5-1: Competency Questions for Concepts in the CIKP-Onto Competency Question Competency Question (used to extract ontology content) (used to evaluation ontology) What other entities are closely related to Given that there is a certain entity closely actors/roles in the construction industry? related to actors/roles in the construction industry, can it be located it in the ontology? What are the specialized or generalized Given that there is a generalized/specialized types of a given entity (other than type for a certain entity (not actors/roles), is actors/roles) in the construction industry? it possible to find it in the ontology? What are the major actors/roles that are Given that there is a certain actor/role that is already (or are not) included in the AR-Onto required by the CIKP but not covered by the and need to be covered by the CIKP-Onto? AR-Onto, is it possible to identify it in the CIKP-Onto? What are the major entities required to Given that a certain entity is required to profile actors and roles for the CIKP profile a given actor/role, is it possible to application? identify it in the ontology? What concepts are required to describe a Given that a certain entity is required to knowledge item (a piece of information or describe a knowledge item, is it possible to knowledge)? identify it in the ontology? What kind of knowledge items are involved Given that there is a certain type of in the construction industry? How are they knowledge item in the construction industry, related? is it possible to identify it in the ontology? What are the attributes of major entities Given that there is a certain attribute of a other than actors/roles? concept other than actor/role (for example, project or knowledge item), is it possible to identify it in the ontology? What are the commonly-used values of Given that a certain attribute has a value attributes? that is usually used in the industry, is it possible to identify it in the ontology? How are the attributes of concepts (other Given that there is a generalized/specialized than actors/roles) grouped? Is there a type for a certain attribute of a concept other superclass-subclass relationship between than an actor or a role, is it possible to find two attributes? it in the ontology?
Similar to the competency questions used in the development of the AR-Onto, competency questions used here have two purposes: (1) to identify the required contents that are to be formulated into the CIKP-Onto, and (2) to evaluate the developed ontology. The questions
Chapter 5: Application Ontology for CIKP 128 for concepts will determine how the concepts in the AR-Onto will be included/extended in the CIKP-Onto and what new concepts should be incorporated into the system and to what extent. Table 5-1 shows the two types of competency questions for major concepts.
5.2.2 Relationship Questions
Table 5-2 shows a set of relationship questions that are used to find cross-tree relations between concepts and evaluate the ontology about relationships. The goal of these competency questions is to profile major actors and roles by revealing the behavioural and functional relationships of actors and roles with other major entities.
Table 5-2: Competency Questions for Relationships in the CIKP-Onto Competency Question Competency Question (used to extract ontology content) (used to evaluation ontology) How do other entities related to a given Given that there is a certain relationship to actor or role? link an actor or a role to other entities, is it possible to indentify it in the ontology? Is a given actor or role involved in any Given that a certain actor or role is involved project, process, or product? in a given project, process, or product, is it possible to identify it in the ontology? How is a given actor or role related to a Given that a certain actor or role is involved project, process, or product? in a given project, process, or product in a specific way, can the ontology tell that? What knowledge items are used to manifest Given that a specific knowledge item is which kind of knowledge? required to manifest a certain type of knowledge, is it possible to identify it in the ontology? What knowledge items are produced by a Given that a certain knowledge item is given process, project, event, actor, or role? produced by a specific process, project, event, actor or role, can the ontology tell? How are knowledge items communicated Given that a certain knowledge item is by actors/roles? communicated by actors/roles in a particular way, does the ontology recognize this?
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5.2.3 Axiom Questions
Axiom questions are gathered based on the particular application of the CIKP-Onto to deduce new knowledge about the information and knowledge needs of actors and roles. Table 5-3 shows those axiom questions.
Table 5-3: Competency Questions for Axioms in the CIKP-Onto Competency Question Competency Question (used to extract ontology content) (used to evaluation ontology) What knowledge items are needed for a Given that a certain actor/role requires a given actor or role in general? specific type of knowledge item, is it possible to identify that in the ontology? What role(s) does a given actor normally, Given that a certain actor normally, occasionally, or never play? occasionally, or never plays a certain type of role(s), does the ontology recognize this? What knowledge items are needed for a Given that there are some knowledge items given actor when assigned a specific role? that are required by a certain actor when being assigned a specific role, is it possible to identify this in the ontology? What knowledge items are needed for a Given that a certain actor/role requires given actor/role in different contexts? different knowledge items in different contexts, does the ontology recognize this? What other actors/roles may a given Given that a certain actor/role may have actor/role have interests about? interests about other actors/roles, does the ontology recognize this? To which virtual community does a given Given that a certain actor is supposed to actor belong? belong to a specific virtual community, is it possible to identify this in the ontology? What are constraints on actors/roles when Given that a certain actor/role has a specific communicating information and constraint in communicaiton, is it possible knowledge? to identify this in the ontology? What are constraints on projects, processes, Given that a knowledge item has a special or products (most importantly, knowledge security clearance or any other type of items) when considering information constraints, is it possible to identify this in exchange and knowledge sharing? the ontology?
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5.3 CIKP-ONTO TAXONOMIES
The taxonomy structure of the domain ontology AR-Onto is utilized as the starting point of the taxonomy of this application-level ontology. There are three top-level classes in the CIKP-Onto and this section will identify them and Appendix G will discuss them in detail - Entity. Entities include all major concepts in the construction industry. They are Actor, Role, Product, Project, Action, and Resource. - Entity Attribute. There are many kinds of attributes included in the upper-level ontology proposed by El-Diraby (2009) and the AR-Onto, and this application-level ontology will focus on the attributes of entities. - Value Pattern. An application-level ontology inevitably needs to deal with instances, and thus, the CIKP-Onto needs to include the values of attributes to define instances.
5.3.1 Entity Taxonomy
The difference between domain-level ontologies and application-level ontologies is that the latter are less universal for the subject domain and include more concepts specific to target application. The main criteria necessary to the building of taxonomies for application-level ontologies are clarity and straightforwardness for the proposed application scope. Entity concepts included in the CIKP-Onto have three sources: - AR-Onto. The concepts of actors and roles have been inherited directly from actors and roles in the domain-level ontology, AR-Onto. In this application-level ontology, those concepts that are not closely related to the construction industry have been removed. In addition, the hierarchical structure is different from that of the AR-Onto. As has been discussed in Section 4.5, the AR-Onto aims at a broad coverage of actors and roles and thus has to follow the structure of the NOC 2006 and the NAICS 2007. However, in the CIKP-Onto, higher priority is given to the clear and straightforward structure needed in developing the CIKP.
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- IC-PRO-Onto and IPD-Onto. These are two domain-level ontologies that share the same upper-level ontology with the AR-Onto. The IC-PRO-Onto (El-Gohary, 2008) is a domain ontology designed to encapsulate the knowledge about processes related to the construction industry, while the IPD-Onto (Hesham, 2007) is a domain-level ontology for products related to the construction industry. CIKP-Onto incorporates within it some of the key concepts in these two ontologies and makes extensions based on the needs of the proposed CIKP system. For example, the product concepts in the IPD-Onto are mainly the physical products of infrastructure systems, but the product concepts in the CKIP-Onto are mainly knowledge items to be exchanged and shared by actors and roles. - Literatures in the construction industry. Domain literatures are the last and most important source for developing the CIKP-Onto. For the concepts that can be obtained from domain-level ontologies such as actors, roles, actions, and products, domain literatures are used to determine the comprehensiveness (how broad the ontology is to represent those concepts) and expressiveness (how specific the ontology is to represent those concepts) of the CIKP-Onto. For other concepts, domain literatures have been used to distil necessary concepts to fulfill the requirements of the proposed CIKP.
5.3.1.1 Actors The higher levels of actor’s taxonomy in the CIKP-Onto follow the design of its domain level counterpart AR-Onto. It keeps two of the three types of actors in the AR-Onto: Individual Actor and Organizational Actor.
Individual Actor Individual actors are one of the major focuses of the CIKP-Onto because they are the subject of information exchange and knowledge sharing in the proposed information portal. The CIKP-Onto includes 546 individual actors that cover the majority of participants in the construction industry. The organization of the hierarchical structure of those individual actors
Chapter 5: Application Ontology for CIKP 132 is similar to that of the AR-Onto but is clearer and more straightforward. See Appendix G.1 for examples of important individual actors.
Organizational Actor In the CIKP-Onto organizational actors are divided into two categories: Government Organization and Non-Government Organization. Government organizations are government offices that regulate the construction industry by making policies and issuing permits. Non-government organizations include for-profit businesses such as construction companies or non-profit organizations such as trade associations or professional associations. There are 181 non-government organizations in the CIKP-Onto and, similar to the AR-Onto, the hierarchical structure is arranged based on industry sectors but only includes organizations that are closely related to the construction industry. See Appendix G.2 for examples.
5.3.1.2 Roles The CIKP-Onto inherits all 174 roles listed in the AR-Onto. The reason that all concepts of roles are included is that this concept is of vital importance when determining the information and knowledge needs of a given actor.
5.3.1.3 Products There are four kinds of products in the upper ontology as defined by (El-Diraby, 2009): Physical Product (physically manufactured or built products such as a crane or a house), Technical Knowledge (products created by knowledge processes such as a plan), Knowledge Item (products that manifest technical knowledge, as for example a set of design drawings to manifest a design which is a technical knowledge) and Decision (products of some actions to decide on). The CIKP-Onto follows the same structure of products and focuses on technical knowledge and knowledge items because it aims at the needs for information and knowledge of industry people and all information and knowledge are exchanged or shared in the form of
Chapter 5: Application Ontology for CIKP 133 knowledge items. Some concepts of physical products are also included in the CIKP-Onto when it is necessary to model the information exchange and knowledge sharing in the construction industry. See Appendix G.3 for major types of products.
5.3.1.4 Action This category of entities defines the main predurant components of a project (El-Diraby, 2009). There are two kinds of actions: Event and Process. A process is a time-consuming predurant entity that engages a set of actors and possibly consumes resources to produce one or more outcomes, which are most likely products. An event also involves actor(s), consumes resources and has outcomes but it occurs at one point in time (or during a relatively short time span). The concepts of actions in the CIKP-Onto have been obtained from an ontology (El-Gohary, 2008) specifically developed for infrastructure and construction actions. See Appendix G.4 for major types of actions.
5.3.1.5 Project Defined by the PMBOK Guide (PMI, 2004), a project consists of a temporary endeavour undertaken to create a unique product, service or result. According to this definition, most endeavours could be treated as projects. Several characteristics of projects have been commonly recognized: a project is normally a one-time job, has a specific scope of work, has clearly defined starting and ending dates, includes multiple tasks, and requires certain resources. There are three kinds of projects defined in the CIKP-Onto: - Construction Project. This is the core type of project that the proposed CIKP system needs to handle. This class includes Building Construction Project, Infrastructure Construction Project, and Industrial Facility Construction Project. These are the basic types of construction projects. Construction projects could also be viewed from other perspectives such as Brown Field Construction Project or Green Field Construction Project.
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- Design Project. Another type of project that is important to the development of the CIKP system is the design project. Design is an indispensable part of the construction industry. Many design projects will be involved in any given construction project, for example Architectural Design Project or Engineering Design Project which can be further divided into Structural Engineering Design Project, Electrical Engineering Design Project, etc. - IT-Related Project. There are some other types of projects that are not a part of the construction industry but support the advancement of the industry. IT related projects are examples of this. It may include Software Development Project or Website Design Project.
5.3.2 Entity Attribute Taxonomy
Entity attributes are used to define the profiles of actors and roles in the CIKP system. The CIKP-Onto has developed attributes for major entities (Actor, Role, Action, Process, Product, and Project). Some of them have been directly inherited from domain-level ontologies and tailored to fit the requirements of the proposed CIKP system. Others have been obtained from domain literatures.
Actor Attribute and Role Attribute Actor’s attributes and role’s attributes have been inherited directly from the domain-level ontology AR-Onto. All attributes developed in the AR-Onto have been included in this application-level ontology because, in the proposed application, the cornerstone intended to support all functions is the profiles of actor and roles, and including as many attributes as possible will provide the system with great flexibility in modelling actors and roles.
Action Attribute Because there are two kinds of actions (events and processes), the CIKP-Onto splits the
Chapter 5: Application Ontology for CIKP 135 attributes of actions into Event Attribute and Process Attribute. Most attributes of processes have been obtained from the domain-level process ontology (El-Gohary, 2008), and more attributes that are necessary for information exchange and knowledge sharing about events and processes have been collected, for example Event/Process Type, Event/Process Starting Time, Event/Process Ending Time, Event/Process Cost, Event/Process Location, Event/Process Subject Domain, etc.
Product Attribute For the product entities, the CIKP-Onto mainly focuses on the attributes of knowledge items because they are the subject to be exchanged and shared in the CIKP system. Major attributes of knowledge item products include: - Affiliation attributes. A knowledge item could have connections or involvement with any of other major type of entity in the construction industry. The affiliations recognized by the CIKP-Onto are Event Affiliation (the knowledge item is about an event, for example, a Meeting Minutes of a Design Collaboration Meeting), Process Affiliation (the knowledge item is about a process, for example, a set of Design Drawings of a Technical Design Process), Product Affiliation (the knowledge item is about another product which is usually a physical product, for example, a Photo of a Residential Building), and Project Affiliation (the knowledge item is about a project, for example, a Change Order Notice of a certain Construction Project). - Computer file attributes. As the knowledge items to be exchanged and shared in the CIKP system are mainly in digital format (in other words, they are computer files), some attributes are gathered to describe computer files. Those attributes are Knowledge Item File Type, Knowledge Item File Name, Knowledge Item File Size, etc. - Other attributes. There are a lot of logical attributes to describe knowledge items and they are pretty much self-explained. Examples are Knowledge Item Author, Knowledge Item Destination, Knowledge Item Copyright Owner, Knowledge Item Creation Date,
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Knowledge Item Creation Time, Knowledge Item Composition Language, Knowledge Item Delivery Method, Knowledge Item Keyword, Knowledge Item Source, Knowledge Item Security Clearance, Knowledge Item Subject Domain, and Knowledge Item Type.
Although physical product is not the focus of this research, the CIKP-Onto still develops some key attributes for the category of Complex Physical Product which are mainly complex buildings, infrastructure systems, or industrial facilities. The reason for this is that they are sometimes necessary in describing knowledge items. For example, if a knowledge item is about a building, then it may be helpful if the system user can (even partially) describe the building itself. Similar to the knowledge items, a complex physical product could have various affiliations with other entities such as Event Affiliation, Process Affiliation, and Project Affiliation. Other attributes for complex physical products could include Product Name, Product Location, etc.
Attributes of other product types are not the focus of this research, and they can easily be added and expanded upon if there is a need in the future for other applications because of the nature of ontology structures. For example, Technical Knowledge could have attributes such as Technical Knowledge Subject Domain, and Decision could have attributes such as Decision Process Affiliation, which relates the decision to a certain process.
5.3.3 Value Pattern Taxonomy
The class Value Pattern is employed in the CIKP-Onto to collect values of some attributes. Sometimes the “is-a” relationship is not transitive in semantics. For example, Communication Language is a kind of Individual Actor Attribute, and English is a kind of Communication Language. However, it is hard to say that English is a kind of Individual Actor Attribute, but is rather just the value of one Individual Actor Attribute. In this case, the
Chapter 5: Application Ontology for CIKP 137 two propositions are both true, but the “is-a” relationships are just not transitive semantically. Another example is the value of subject domain. Knowledge Item Subject Domain is one of attributes of Knowledge Item, and Electrical Engineering is a subclass of Subject Domain, but it is not to say that Electrical Engineering is an attribute of Knowledge Item, but is rather the value of one attribute of Knowledge Item. Appendix G.5 shows some major value patterns included in the CIKP-Onto.
5.4 CIKP-ONTO RELATIONSHIPS
The relationships included in the CIKP-Onto inherit the structure and schema of its domain level counterpart, AR-Onto, and mainly expand cross-tree relationships. The relationships in the application level focus on the requirements of the proposed application and should be capable of encapsulating the knowledge in that application area. Because the objective of the CIKP system is to facilitate the information exchange and knowledge sharing based on the nature of actors and roles, there are several principles involved in defining relationships in the CIKP-Onto.
The first principle is to model profiles of industry participants. The relationships that are to be used to model the profiles of actors and roles take top priority. Profiles of actors and roles are the cornerstone of the CIKP system in determining the information and knowledge needs for each system user, and thus it is necessary to have sufficient relationships to flexibly model all important actors and roles. These are mainly cross-tree relationships obtained from the literatures of the construction industry and the O*NET system (2008) where many pieces of occupation-specific information such as workforce characteristics and occupational requirements are available.
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The second principle is to express axioms. The relationships that are necessary to express axioms and rules to deduce new knowledge are important. One key reason for using ontologies in applications is the ability it affords to deduce new knowledge from populated ontologies and axioms. For example, it is reasonable to assume that if a knowledge item is the change order of the electrical design for project ABC and actor XYZ is the electrical contractor of the same project then actor XYZ will have information needs about this knowledge item. In order to express such an axiom in the ontology, the ontology should first have the ability to deal with the relationships involved in describing the knowledge item: “knowledge item type” (the value is Change Order), and “project affiliation” (the value is ABC), “knowledge item subject domain” (the value is Electrical Engineering), and the relationship to describe the actor: “role” (the value is Contractor), “role subject domain” (the value is Electrical Engineering), and “project affiliation” (the value is ABC). These are the basic building blocks needed for constructing axioms.
The third principle is to describe more entities. Some new relationships are introduced by the involvement of more entity concepts than just those of actors and roles. In the domain-level ontology AR-Onto, only relationships that are closely connected to actors and roles are modelled. There are many new entity concepts involved in the CIKP-Onto to model the application of information exchange and knowledge sharing, and thus, some new relationships are required to achieve the purpose. For example, the relation between Knowledge Item and Technical Knowledge is expressed by Technical Knowledge “is manifested by” Knowledge Item. Another example would be Owner “awards” Contract.
5.4.1 Attributive Relationships
Attributive relationships are important to describe all entity concepts. For each major entity concept, there is a set of attributive relationships to describe the concept. In most
Chapter 5: Application Ontology for CIKP 139 circumstances, an attributive relationship is related to an attribute concept in the taxonomy. For example, Experience is one of an actor’s attributes, and thus there is an attributive relationship “has experience” for actors. Figure 5-2 shows an example of attributive relationships for actors/roles and knowledge items.
Figure 5-2: Example Object Attributive Relationships
In addition to the explicit assertion of relationships, fuzzy relationships are utilized to deal with the dynamics of the construction industry. For example, in most cases an Electrical Engineer “has experience” of Electrical Design Experience. However, some electrical engineers who work for a government agency such as an Electrical Inspector may not have any electrical design experience. In order to handle this situation, a fuzzy relationship “could have experience” is introduced. In an application-level ontology such as CIKP-Onto, the relationship “has experience” is not always true in all circumstances. Therefore, the introduction of this kind of fuzzy relationship offers great flexibility for dealing with industry dynamics. In the CIKP framework, for a given actor or role, the system can define the profile
Chapter 5: Application Ontology for CIKP 140 by “could have” attributes and leave the system user to determine whether to take this relationship or not.
Figure 5-3: Example Data Type Attributive Relationships
The above-mentioned attributive relationships are cross-tree relationships linking two concepts in the ontology. These are called object attributes. There is another type of attributive relationship, which links a concept to a type of data such as Integer, String, or Time. This kind of attributes is called datatype attributes. For example, the relationship “has name” of actors will link a given actor to a string which is the name of the actor. Figure 5-3
Chapter 5: Application Ontology for CIKP 141 shows an example of data type attributive relationships.
5.4.2 Other Cross-Tree Relationships
Figure 5-4: Partial View of Cross-Tree Relationships
In addition to attributive relationships, there are some other types of cross-tree relationships to link concepts in the CIKP-Onto. In accordance with the three principles discussed at the beginning of Section 5.4, these relationships are used to describe the knowledge of concepts, express axioms to deduce new knowledge, or model the profiles of actors and roles. It is
Chapter 5: Application Ontology for CIKP 142 impossible to exhaustively cover all these kinds of relationships in a single ontology, but the CIKP-Onto includes the most important ones based on the three principles described. The nature of ontology structure makes it an easy job to enrich this kind of relationship at a later date if necessary. Figure 5-4 shows a partial view of these cross-tree relationships implemented in the CIKP-Onto.
5.5 CIKP-ONTO AXIOMS
There are two kinds of axioms in the CIKP-Onto: one kind consists of the restrictions of relationships and the other kind contains the rules that could deduce new knowledge. They are applied in different situations for different purposes.
5.5.1 Restrictions
Restrictions are used to restrict the relationships defined in the ontology based on the context of application. For example, there is a relationship “has experience” and by default the subject of this relationship is anything and the object can also be anything. In other words, anything could have experience in anything. However, in the context of CIKP, it would be more reasonable to restrict the subject of this “has experience” to be either actors or roles (the union of class Actor and class Role), because in the niche of CIKP other entity concepts such as process, project, or product are not supposed to have any experience. Similarly the object of this relationship could be restricted to the class Experience Value which is a subclass of Value Pattern.
The above-mentioned restrictions are applied for cross-tree object relationships that link two concepts. For datatype relationships, restriction on the subject could be classes in the
Chapter 5: Application Ontology for CIKP 143 ontology and the restriction on the object could be a certain type of data. For example, there is a “has process starting time” relationship to define the starting time of a certain process. The subject of this relationship will be restricted to the class Process, as it is not supposed to have any other entity to have a “process starting time”. As for the object, it will be restricted to a data type called Time, which means only a kind of time will be allowed being the object of this relationship. Similar data type restriction is the subject of the relationship. “has name” should be the union of Actor, Role, and Product, and the object of this relationship will be a datatype of String.
Another kind of restriction is the limit on the cardinalities of a certain relationship on a given concept. This kind of axiom has been discussed in Section 4.6 where the axioms of the domain-level ontology AR-Onto is discussed. This kind of restriction is still applicable to the application-level ontology CIKP-Onto. For example, a Knowledge Item has “exactly 1” Creation Date and “at least 1” Knowledge Item Key Word.
Restrictions can also be used to assert that two concepts are equivalent. For example, in the class Accountant, we can say that the concept CGA is equivalent to the concept Certified General Accountant. In this case, the ontology can restrict the concept CGA as a subclass of the concept Certified General Accountant and, at the same time, the concept Certified General Accountant is a subclass of the concept CGA. The only way to satisfy these two constraints is to recognize that the two concepts are equivalent.
5.5.2 Rules
A rule (of inference) is a function from sets of formulae to formulae. With relationships as the building blocks, it is easy to compose rules using First Order Logic (FOL). FOL is a formal deductive system used in mathematics, philosophy, linguistics, and computer science. Unlike
Chapter 5: Application Ontology for CIKP 144 natural languages such as English, FOL uses a wholly unambiguous formal language interpreted by mathematical structures. An example of writing rules in FOL is shown below.
Sample rule expressed in English (natural language): If there is knowledge item A, which is a change order of project B issued by an electrical engineer C, and actor D is a electrical contractor for the same project, then actor D should need to know knowledge item A.
Sample rule expressed in FOL: ∃A, B,C, D,E Change _ Order(A) ∧ has _ Knowledge _ Source(A,E) ∧ Actor(E) ∧ has _ Role(E, Electrical _ Engineer) ∧ has _ Pr oject _ Affiliation(A,B) ∧ Pr oject(B)
∧ Actor(D) ∧ has _ Role(D,Electrical _ Contractor) → has _ Information _ Knowledge _ Need(D, A)
This kind of rule as used in the CIKP-Onto is mainly for inferring knowledge about what kind of knowledge items a given actor or role needs. This can be achieved in two ways. One way is that just illustrated in the example of the electrical contractor. In this way, knowledge about information and knowledge needs is obtained from satisfying a set of conditions which may or may not be true depending on the specific case. Another way is through the profile of actors and roles. For example, if actor A is an architect then this actor will always have a need for information and knowledge about architectural codes. This axiom could be written in FOL:
∀A Architect(A) → has _ Information _ Knowledge _ Need(A, Architectural _ Code)
One of the advantages of ontologies is the flexibility of adding axioms without changing the structure of concepts and relationships. As such, it would be a very easy job to modify or add any rule to the CIKP-Onto. In the CIKP-Onto there are about 80 rules to infer information and knowledge needs.
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5.6 CIKP-ONTO CODING
The CIKP-Onto is implemented by ontology editor Protégé (version 3.4). Similar to the implementation of the domain-level ontology AR-Onto, concepts are coded as classes, and relationships are coded as properties. Restrictions in the CIKP-Onto have been implemented in the Protégé-OWL. The implementation of rules has been done by the Semantic Web Rule Language (SWRL) which has a plug-in tag in Protégé. The reason for selecting SWRL to implement rules and JESS to reason rules has been discussed in Chapter 3.
Chapter 5: Application Ontology for CIKP
6 CIKP FRAMEWORK 6
he Construction Information and Knowledge Portal (CIKP) was developed in order to Tshowcase the use of an ontology designed to improve the communication of information and knowledge in the AEC industry. As stated before, the incorporation of semantics into communication systems will enhance their usability and usefulness because the computer systems involved will be able to explicitly understand both the meaning of the contents being communicated and the needs for information and knowledge on the part of system users. As a prototype application, the CIKP aims at illustrating the use of an ontology in enhancing communication in the AEC industry but not its formal or commercial applications.
146 147
The first step in the development of the CIKP framework is to define the scope of the work. In many circumstances, the term “framework” is generally used to describe a basic conceptual structure. This very vague definition has allowed the term to be used as a buzzword that needs to be clearly defined in a technical manner for a given ambience in a certain application. In this research, a framework means a conceptual framework that is used to present a preferred approach (or a series of approaches) to an idea or thought. Specifically it includes the methods, approaches, and features needed to showcase the use of an ontology. Consequently, the development of policies (such as use, efficiency and security) is outside of the scope of the portal. It should also be noted that communication systems should not be limited to a portal or a software application. Such tools should be complimented with changes to work processes, and indeed, work culture. This issue is beyond the scope of this research. Nonetheless, this thesis does provide some generic guidelines for incorporating some potential applications in this regard.
Several tasks have been identified in the development of the CIKP framework: requirement analysis, social involvement definition, system architecture and matching algorithms, and basic function definition. Requirement analysis will identify intended users, service scenarios, and the technical approaches to be used (semantic web and ontology, Pub/Sub systems, and Social Web practices). Social involvement definition will determine the social involvement approaches that are useful to the CIKP framework. There are many social involvement approaches (for example, commenting, tagging, blogging, wiki, rating, voting, etc.) but not everyone is an appropriate candidate. Thus, the incorporation of the social involvement approach must be selective. Portal system architecture will reveal the high level description of the Pub/Sub system used in terms of its matching algorithms. The major task of the development of the CIKP framework is to define and design basic functions in order that this information system may fulfill its objectives. At the end of this chapter, the CIKP is compared to the existing information systems being used in information and knowledge
Chapter 6: CIKP Framework 148 management to show the CIKP’s advantages as conclusively as possible.
The CIKP framework is a generic system designed to address the issue of communicating information and knowledge in the AEC industry and its current focus lies within the area of communication between professionals at the technical level due to the scope of knowledge encapsulated in the two ontologies. By changing the ontology accordingly, it can be customized to accommodate special industrial communication needs outside this area such as those of public consultation. It could also be implemented at any application level such as the project level (a group of people working on a certain project), the intraorganizational level (within a certain organization), the inter-organizational level (within several organizations), or even the industry level (among all people from any organization). The CIKP framework can also be embedded in existing business processes if required. Many organizations have already established their own IT systems in helping to promote the communication of data, information, and/or knowledge, and they may not want to have another system but only to incorporate the CIKP framework into their existing business model.
6.1 REQUIREMENT ANALYSIS
Requirement analysis in systems engineering involves the tasks that help to determine the needs or conditions to be met for the system to be developed. It is critical to the success of a development project (Abran et al., 2004) because the primary measure is the degree to which the system meets its intended purposes. In the domain of software engineering, requirement analysis has received much attention and a formal branch called requirement engineering has been studied by many researchers. Zave (1997) indicates that requirement engineering concerns “the real-world goals for, functions of, and constraints on software systems”. Zave’s perspective presents the “why”, “what” and “how” of a system. Although requirement
Chapter 6: CIKP Framework 149 engineering stems from software engineering, its many principles are applicable to most systems engineering work, including the development of the CIKP framework. This section will address three issues related to requirement engineering: identification of intended users, identification of services, and the determination of applicable technical approaches.
6.1.1 Identification of Intended Users
In general, the intended users of the CIKP are any industry participants in the AEC industry with needs for information and knowledge. The participant has been defined to be either an individual (a person) or an organization (someone, such as an agent, who will be representing the organization in the system). It has been recognized that, in the AEC industry, some people are more information- and knowledge-intensive than others. People who tend to be dependent on information and knowledge in their daily work include all kinds of professionals who are closely connected to the AEC industry, such as architects and engineers. Most administrative and management roles also largely depend on effective information and knowledge communication in their work, such as project managers, construction managers, financial managers, etc. Industry participants who do not require frequent information and knowledge communication are either those who are not closely related to this industry (for example, lawyers and accountants) or workers who are mainly physical workers (for example on-site construction labourers).
In the development of the CIKP framework, professional people who are intensively involved in construction projects are treated as the core of the intended users. There are no clear boundaries between information- and knowledge-intensive industrial players and the rest players, because the needs for information and knowledge are determined by both a system user’s intrinsic attributes (the actor) and extrinsic attributes (the role that actor is playing), both of which are very dynamic in the AEC industry. Although it is not easy to tell
Chapter 6: CIKP Framework 150 who will need the CIKP more and in what circumstances, it is necessary to roughly limit the scope of intended users, just because this industry is too big, and because the resources (time and budget) available for this research is limited. It is a safe assumption to focus on professional people in developing the CIKP framework.
6.1.2 Services Goal and Scenarios
Our goal in developing the CIKP as a tool to facilitate information exchange and knowledge sharing in the AEC industry is to deliver the right information and knowledge to the right party in a timely manner. Due to the difficulties identified in Chapter 1, this is not an easy job. Three services must be effectively performed if this goal is to be achieved: (1) the semantic linking of people with project information, (2) the semantic linking of people with domain knowledge, and (3) the social linking of people with their peers. Based on these three requirements, three service scenarios have been suggested in the CIKP framework.
In the first service scenario, various individuals and organizations coordinate actions with each other by effectively exchanging project information and knowledge. Each industry participant (either an individual or an organization represented by someone) is treated as an actor in the system. The system will inform its users of the information and knowledge that they really need in a timely manner, based on their profiles (the actor they claim to be and the role that the actor is playing). In this way, people are linked with project-specific information.
The second service scenario envisions the sharing of domain-specific knowledge. The experience and lessons learned in the course of construction projects, whether successful or not, should be kept and shared among the people who need them. Knowledge sharing will help the industry to avoid mistakes from previous projects and people will not have to constantly reinvent the wheel. In this way, people are linked with domain-specific
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The last service scenario supports the knowledge learning and sharing process through the creation of virtual teams or Communities of Practice (CoPs). For a variety of reasons, industrial people have interests in information and knowledge not only about projects and science, but also about other people in industry. They may want to find people to work together, share experiences and knowledge, etc. The CoPs link people with other people with similar interests in a particular subject.
Because the CIKP framework is a generic system and does not have a specific application niche, the development of the framework should be flexible enough to accommodate any possible application environment. For example, each of the three service scenarios should be designed in modules so that the actual implementation could take any module that is desired and leave the others for future expansion. In case the CIKP framework is hired for communicating project information only, there is no need to implement the other two service modules.
6.1.3 Requirements of Technical Approaches
In order to fulfill the expectations in the above-mentioned three-service scenarios, the proposed CIKP framework incorporates within it the advantages of three technologies: a pub/sub system to loosely couple knowledge publishers and subscribers, Semantic Web technology to communicate real meanings, and the Social Web concept to harness collective intelligence.
Publish/subscribe is an appropriate communication paradigm for handling a very fragmented industry such as construction. First, publish/subscribe systems do not require any
Chapter 6: CIKP Framework 152 participating entity to be coupled with another in either time or space. This feature fits the fragmented AEC industry very well because it is nearly impossible to synchronize time and space among industry participants just for the purpose of knowledge management given their high mobility and competitive characteristics. Second, publish/subscribe systems can be deployed at any level and no participating entity needs to specially develop or invest anything, but only have the willingness to take part in the system. This means that the CIKP can be applied at the organizational, regional or industrial level. Third, the communication paradigm of publish/subscribe systems encourages a culture of sharing, because each participating entity shares with the whole and learns/benefits from the whole.
Although publish/subscribe systems are good candidates for communicating information and knowledge in the AEC industry, there is still room for performance improvement. In order to enrich the expressiveness of events (publications or subscriptions in publish/subscribe systems), Semantic Web technology is incorporated in the CIKP framework through the development of ontologies (a formal and interoperable knowledge representation). Generally speaking, having a set of interoperable ontologies as a common ground for this industry allows for the explicit handling of tacit knowledge and different systems can understand each other given that they are based on common ontologies. With the help of ontologies, publish/subscribe systems will be able to perform smarter matching because of the semantic expression of events. Ontologies also can help users in formulating subscriptions. One common assumption for publish/subscribe systems is that users know with certainty what to subscribe to. When a user has no idea or is not sure about what information and knowledge they should subscribe to, ontologies will be able to reason his/her profile and generate new knowledge about his/her potential needs for information and knowledge, then formulate subscriptions automatically on behalf of that user.
Another assumption adopted by many publish/subscribe systems claims that every published
Chapter 6: CIKP Framework 153 event is good and free to share. This is a questionable assumption even in closely controlled publish/subscribe systems. It is more dangerous in a system open to the general public such as the CIKP, because a broad range of industrial people will be granted full rights to publish any content and claim any attribute of published contents. Social Web concepts are introduced into the CIKP system to enrich the description of events by several social interaction approaches. Each published item can be socially reviewed, tagged, voted, and commented upon by many peer users, so that any missing information can be added, any wrong information can be corrected, and any malicious information can be removed. This will effectively enrich the quality of expression of any published events and thus improve the Quality of Service (QoS) of the CIKP.
6.2 SOCIAL INVOLVEMENT METHODS
After carefully reviewing popular social web concepts and approaches, a set of social involvement methods has been incorporated into the CIKP framework, including social tagging, commenting and voting. There are several other social involvement methods mentioned in Chapter 2 but not incorporated into the CIKP framework for different reasons – some are not suitable for the scope of this research, and others are not technically feasible at this time. This section will discuss the social interaction methods that are included or not included in the CIKP framework.
6.2.1 Social Interaction Methods Included in the CIKP Framework
Social tagging has been discussed in Section 2.4.4. This method has been employed in the CIKP framework as the major approach to enrich the semantics of events. Users can place tags on any published items to express their understanding as complementary information to
Chapter 6: CIKP Framework 154 its original description. Also, users can place tags on the profile of any other user to further describe him/her. Users will be able to select tags from a section of the ontology structure (mainly the Attribute class), and fill out the value for that selected tag before introducing it into the system.
Social commenting means that users can share their opinions about any published item using their own natural language by writing a short comment on the page of that item. This method has been very popular since the advent of the online discussion board. Placing tags is a way to express opinions and understanding, but it may not be enough to comprehensively provide supporting information such as any reason of placing the tags, any evidence to support the tags, possible changes to better describe the knowledge item, etc. Social commenting gives people a chance to discuss any published item in much greater detail.
Social voting means that users can express their opinions by agreeing, disagreeing or flagging something. In the CIKP framework, users are able to have a say not just on published items or the profiles of other users, but also the comments or tags about them as contributed by other users. System users are able to agree with the comments/tags that they support, disagree with the comments/tags that they dislike, and flag the comments/tags that they feel are inappropriate (offensive, off-topic, spam, etc.) There are two distinct advantages in this kind of social involvement in the CIKP framework. The first is that the original publisher of a given knowledge item or a user profile will see the judgement on any comments and tags from the rest of the system’s users, and, in turn, will be able to make a better decision about approving/rejecting a tag or modifying the description of the knowledge item or the profile. The second is that general system users will understand the comments and tags much better by knowing the opinions of others when they are not sure about those comments and tags.
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6.2.2 Social Interaction Methods not Included in the CIKP Framework
Social rating is a great way to help the general public to quickly sort out items. However, it is not easy to implement this method in the CIKP framework. Technically it is not hard to allow users to rate each item, but the creditability of this kind of rating is questionable, because everyone has an equal right to rate, and because the system is not able to differentiate between the special expertise of some voters and that of others. The level and quality of expertise are very important because users are voting on knowledge items, and ideally, the votes from experts in the area of interest should gain more weight in calculating the ratings. Due to the limited time frame, voting is not included in the CIKP framework.
Social networking is partially included in the CIKP framework through the creation of CoPs, although the functions of social networking are limited. Currently, system users are able to send a piece of information or knowledge to all group members of any CoP by assigning the suggested destination, but there are no other social networking functions suggested. Deeper social networking interaction should include finding and connecting with friend’s friends, following up friends’ events and thoughts, communicating through private messages, etc. There are two reasons why the CIKP framework did not include those social networking functions: (1) there is a limited time frame, and (2) those functions are not the core focus of the application scenarios in question. Blogs and wikis are also lie outside of the CIKP framework for similar reasons.
6.3 SYSTEM ARCHITECTURE OF CIKP PROTOTYPE
The CIKP prototype system has been developed by a research team at Zhejiang University, China. Figure 6-1 shows the architecture of CIKP prototype. There are three modules: Web Interface Module, Database Module, and Core Module.
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Figure 6-1: System Architecture of CIKP Prototype
The Web Interface Module receives data of web pages and allows the interaction between users and the system. Interface is defined and served as a bridge between the Web Interface Module and other two modules. The implementation is made with Struts and Spring. Struts allows the use of Java classes that can be called by determined Actions. The interaction between the browser requests to actions and subsequent Java calls is made by the SPRING framework. Ext JS is used to handle the logic and Dojo to represent dynamic visual components in the system.
The Web Interface Module provides an ontology tree that display all concepts in the ontology file in a hierarchical structure. The tree is built with the Dojo API and obtains the data for display through a Struts Action upon a remote call in Javascript. It receives the data as a JSON Object and populates a Dojo Tree Widget. The widget make it possible to define a class (for example, an Individual Actor) based on all sub-classes of that class. It is realized by making the ontology tree to correspond to the URI of classes. The JSON Object only needs to bring the class name and its URI because the selection of a leaf node on the ontology tree provides the Javascript controller the URI selected, which can be sued to populate the main
Chapter 6: CIKP Framework 157 interface with the properties of a class.
In the Database Module, MySQL manages all CIKP databases. It deals with all CIKP data: client registries, knowledge item management, subscriptions management, ontology handling, and (some) validations. A Java database connection (JDBC) is a database driver that enables the connection to a database programmatically. JDBC is used to connect the Web Interface Module and MySQL database.
The Core Module handles the logic of interacting between database tables in MySQL and ontology in Jena API with the visual representation that appears in the Client. JTangPS Core
Model is a publish/subscribe model developed by Zhejiang University which is a light weight
J2EE application server, similar to BEA WebLogic, IBM WebSphere and JBoss (Shi, et al. 2007). In this prototype system, the JTangPS Core Model provides publish and subscribe services to the Web
Interface Module. JTangPS Core Model consists of publish/subscribe service layer, management layer, event matching layer as shown in Figure 6-2. Publish/subscribe service layer is the service entrance of the publish/subscribe system. It provides subscribe service, publish service and notification service. Management layer consists of four components. Subscribe management component is responsible for the storage and retrieval of subscriptions and event management component is responsible for the storage of the events which are the knowledge items and user profiles here. Client management component is responsible for the management of registered subscribers. Service management component is responsible for the security, transaction, QoS, and other additional services. All the necessary information is stored in MySQL database through JDBC. Event matching layer is responsible for matching events to subscribers. Event matching algorithm can be replaced for different implementations. Reasoning is realized in the CIKP prototype system by Jena API and Pellet Reasoner. There are several reasoners available, such as Pellet, Racer, and Fact++, and the reason this prototype system select Pellet is it works great with Javascripts. Jena API
Chapter 6: CIKP Framework 158 communicates with ontology file and then Pellet is called to reason SWRL rules. The new facts generated are sent back to ontology and displayed to system users through Web Interface Module. The core functions of publishing and subscribing are realized by the JTangPS Core Model.
Publish/Subscribe Publish Subscribe Notification Service Layer Service Service Service
Subscription Event Management Management Management Layer Client Service Management Management
Event Matching Semantic-aware Content-based Layer
Figure 6-2: JTang Core Model
6.4 MATCHING ALGORITHMS
The matching between sources (information and knowledge providers) and receivers (information and knowledge consumers) is achieved in four different ways: user subscription, user query, rule reasoning, and semantic reasoning.
User subscription means system users can define their needs by subscriptions and the system will return to users with the information and knowledge that matches the desired subscriptions whenever a match appears in the system. This is the push mode in communication where the information and knowledge is “pushed” by the system or the source to the receiver. In traditional Pub/Sub systems, in order to match a publication and a subscription, the conditions listed in the subscription have to be fully satisfied. However, the
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CIKP system assesses the level of matching when the condition is about a concept in the ontology. For example, someone may express a condition (Author, Structural Engineer) in a subscription where the user wants to find something with an Author who is a Structural Engineer. In traditional Pub/Sub systems, this condition has to be exactly and fully stratified in a publication in order to have this publication sent to the system user. The CIKP system, on the other hand, will look up the ontology for the concept Structural Engineer, and then assign a value (from 0 to 1) to all concepts that are a superclass or subclass of Structural Engineer. (For example, Civil Engineer will have a value of 0.8 assigned, Engineering Professional will have a value of 0.5 assigned, and Professional will have a value of 0.2 assigned.) If there is a publication that has an author who belongs to a superclass or a subclass of Structural Engineer, it will be considered a matched item in the CIKP system for this condition but with a matching index measured by the relation between the concept indicated by the user in the subscription and the concept declared in the publication. For an exact matched condition, the matching index will be 1. The algebraic sum of all individual matching indexes will be treated as an overall matching index based on which match level for all publications will be evaluated and returned to the user.
User query is the pull mode of communication where a system user initiates a query and the result of this query will only be returned to the user once (unlike the push mode described in a user subscription where the matched publications will be detected and returned to the user once they appear in the system.) The calculation of an overall matching index for each publication is the same as that descried above. The only difference between user subscription and user query is that the subscription will enforce the system to act as an agent continuously looking for matched items while query will only perform this function once.
Rule reasoning means that the system will reason out the pool of information and knowledge and the profile of a system user, and based on the result of this reasoning, infer the needed
Chapter 6: CIKP Framework 160 publications based on a set of rules written in SWRL. Theoretically the SWRL rules will work in two ways: (1) it can directly conclude the need(s) for a certain information or knowledge by populating the property has_Information_Need, or (2) it will infer a new knowledge assertion based on that the CIKP system could perform a better matching search. SWRL rules as incorporated into the CIKP system are collected from the industry and reflect the domain knowledge commonly agreed upon in the AEC industry. The CIKP system is different from any of expert systems because those SWRL rules are not hard encoded in the system but are stored in a separate knowledge base associated with the ontology used in the system. A system administrator (even system users, if policy allows so) could easily update the SWRL rules whenever needed. The publications matched based on rule reasoning could also be indexed with the algorithm described above.
Semantic reasoning is an automatic and dynamic matching approach based on frequently repeated key words appearing in the knowledge items. Although rule reasoning is better than expert systems due to its flexibility in encoding and modifying rules, it is still considered a static way of matching publications and subscriptions because SWRL rules have to be pre-defined in the system in order to perform a better matching search. In the semantic reasoning approach, a set of Frequently Repeated Key Words (FRKWs) are identified from a publication. This set of FRKWs will be located in the taxonomy and the system will examine the semantic proximity (Ri) between any FRKW and the concept used in the description of a system user’s profile. Ri is measured by the shortest path between the two concepts in the taxonomy (measured by Ric) and the relationship between the concept and actor type or the role type claimed by the user (measured by Rir.) The relevance of a given publication to a system user (R) will be assessed by the sum of Ri for all FRKWs. Ric represents the taxonomic similarity between two concepts (Budanitsky and Hirst, 2000) and Rir represents the relation of a FRKW to a particular system user (the degree to which the user relates to the
FRKW). Ri is calculated as the product of Ric and Ric, and R = ∑ Ri = ∑(.Ric × Rir ) ii
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Figure 6-3 shows an example in determining Ri. In this case, a Transportation Engineer plays a role of Project Designer in an Infrastructure Project that produces a Highway product. We assign the value of Ric as follows: the most direct subclass or superclass will have a value of 0.7, the second most direct subclass or superclass will have a value of 0.4, and the third most direct subclass or superclass will have a value of 0.2. Any level after that will have a value of zero. We assign the value of Rir as follows: the most direct relation with the system user will have a value of 1.0, the next relation level will have a value of 0.8, the third relation level will have a value of 0.5, and then the value will be reduced to 0.2 at the next level. Any level after that will have a value of zero. In the CIKP system, the values of Ric and Rir at each level are adjustable. If there is a FRKW Freeway, then the Ri of this FRKW Freeway for a user profile defined as Transpiration Engineer is Ri = Ric x Rir = 0.7 x 0.5 = 0.35.
Figure 6-3: An Example of Assigning Values to Ric and Rir
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The author would like to indicate two points related to matching algorithms. First, the system will match a subscription or query with publications that have been socially annotated. The advantage of social involvement is the power of collective intelligence. The CIKP system has been designed to automatically update the description of a publication as soon as the social opinions have been approved or accepted by the original publisher. As such, any matching attempt will be based on the combination of the understanding of the original publisher and the social opinions collected after the publication is posted, and over time, social opinions will gradually come to outweigh in value the descriptions of the original publisher alone. Second, with the semantic reasoning approach, it is not claimed that a set of FRKWs is the best way to represent the content or the meaning of a piece of knowledge, but it does have a good chance of better handling publications that are not able to be automatically processed by computer using advanced natural language processing methods.
6.5 CIKP FUNCTIONS
This section will focus on the major functions defined in the CIKP framework: define/edit user profiles, publish information and knowledge, subscribe to interested items, socially interact with published items or user profiles, manage user home pages, and query the system. Those functions will be discussed through a set of key web pages in the CIKP prototype system.
6.5.1 Define and Edit User Profiles
A user needs to register first in order to obtain an account to use the CIKP. Figure 6-4 is the web page that a registered user will see when he/she goes to define his/her profile. On this
Chapter 6: CIKP Framework 163 web page, a registered user will be asked to create his/her profile before performing any other tasks. A user will be able to define his type of actor by dragging a concept (under the Actor class) from the ontology tree located on the left to the text field where the system asks him/her to “select a type of actor from the ontology to describe yourself”. In this way, the system will know who the user is.
Figure 6-4: User Profile Definition (Blank)
The system will show by default all mandatory attributes in blue color for a generic actor (for example, Given Name, Family Name, Language, etc.) according to the definition in the ontology. Based on the type of actor a user selects, this web page will dynamically update those mandatory attributes, and more important, values of some attributes will be suggested. For example, if the actor class Architect is selected, then the value of the attribute Knowledge will be suggested by the system such as Architectural Design Knowledge or Building and
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Construction Knowledge. Users will be able to add (or remove at a later time) any optional attributes by clicking on the “Edit Attributes” button. All valid attributes for the type of actor selected will show up in a pop-up window and users will be able to select (or deselect) any of them to further define their profiles.
Figure 6-5: User Profile Definition (Populated)
So far the system has known who a user is (the type of actor) but, in order to make the system achieve better matching, the user still needs to let the system know his/her role or roles in different contexts. Defining a role and its associated attributes is not mandatory in the CIKP framework, because not every system user will necessarily be actively participating in any context within the AEC industry (for example, a recently graduated college student who is
Chapter 6: CIKP Framework 165 unemployed). However, if a user does want to define the role(s), he/she can push the “Add Role” button in Figure 6-4, and a field similar to the definition of actor type will show up on the web page (see Figure 6-5). Here the user can drag a concept (under the class Role) from the ontology and place it in the text field where the system asks him/her to “select your role from the ontology” and define any attributes associated with the selected role type to further enrich the definition of this user’s profile.
Figure 6-5 is an example of a populated profile. This user is an electrical engineer, who has the information that is related to his type of actor as defined on the web page, such as name, gender, language, location, technical domain, knowledge, and skills. In addition, this user defined his role in two contexts: a construction project that he is currently involved in and the organization that he is working for. In project ABC, Peter Chan is working as a Project Designer who has the right to select materials in his field and is responsible for technical design and on-site support. Mr. Chan is an employee of XYZ Consultants and his position in the company is a Department Supervisor. He has served the company for nine years. Some other basic information about the organization, such as the address and contact number, is also defined. By clicking the “Edit Attributes” button in each panel of role definition, the user can define more optional attributes for the role selected. Any optional attributed added here could be removed at a later time if the user wants to do so.
System users are able to revisit their profiles as defined at a later time and edit them as desired. They can add or remove any role or any attribute associated with a particular type of actor or role(s). They also can modify the value of any given attribute. The only thing that system users are not able to do is remove the type of actor and the mandatory attributes associated with the class Actor. This indicates that a system user must be an instance of a certain type of actor and declare associated attributes in the CIKP system.
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6.5.2 Publish Information or Knowledge
One of the most important functions for all system users of the CIKP is to be able to publish something that they are willing to share with the community. In the ontological model of the CIKP-Onto, any piece of information or knowledge is manifested as a Knowledge Item, which is a kind of Product. Figure 6-6 is the web page a system user will see after pushing the “Publish” button in the “Function Menu” panel on the left, and then select a type of knowledge item from the ontology tree and drag/drop it in the main area on the right hand side.
Figure 6-6: Publish a Knowledge Item (Blank)
At the beginning, there will be only one line to ask users to select the type of knowledge item to be published from the ontology tree under the class Knowledge Item. After a type of
Chapter 6: CIKP Framework 167 knowledge item is dragged by a user to fill the text field, the CIKP will look up the ontology CIKP-Onto to find all mandatory attributes for the knowledge item selected, and display them on the screen. The values of some attributes will be automatically assigned by the system if they can be obtained from the computer, such as the publishing date, publishing time, publisher (the current user), etc. The publisher can push the “Edit Attributes” button to edit optional attributes for this knowledge item and assign values if necessary. This knowledge item is actually a digital file in the publisher’s computer that enables the publisher to find and upload the digital file to the system by pushing the “browse” button. This publication will be saved in the CIKP and available for other system users to browse or subscribe as soon as the publisher pushes the “Save” button.
Figure 6-7: Publish a Knowledge Item (Populated)
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Figure 6-7 is an example of a populated publication. The current user (Peter Chan) wants to share a knowledge item – a Change Order Notice – with the community. Values of some attributes have been obtained automatically by the computer and are greyed out (users are not able to modify the values if they are obtained automatically). Through the attributes and their values, Mr. Chan has told the system that the change order notice is from Project ABC in the domain of electrical engineering and has keywords “low voltage wiring”. The actual change order notice has been uploaded as an attachment of this knowledge item.
System users could indicate the desired recipients of any published knowledge item by assigning the value to the “send to” attribute. The value of this attribute is a group of system users defined by some of their attributes. For example, it could be a group of users who claim that their type of actor is Electrical Engineer or a group of users who are involved in Project ABC (the value of the attribute “has_Project_Affiliation” is Project ABC). In this way, a publisher could actively push a knowledge item on to some desired recipients even though they might not subscribe to this knowledge item.
6.5.3 Subscribe to Published Items
Similar to the web page for publishing information and knowledge, the web page for subscribing will ask system users first to indicate what type of knowledge item they are going to subscribe to. In Figure 6-8, system users can drag any desired type of knowledge item from the ontology tree under the class Knowledge Item to the text field in the main window. This tells the CIKP that any published item, which is defined by its original publisher to be of the same type or sub-type, is a candidate to be screened under certain conditions.
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Figure 6-8: Define Subscription (Blank)
Figure 6-9 shows an example of a populated subscription. Then, as with the function of publishing, as soon as the type of knowledge item is selected, the system will show a set of mandatory attributes for that type of knowledge item based on the knowledge encapsulated in the CIKP-Onto ontology. In this example, the user wants to subscribe to some documents, and the system indicates that any document should have keywords and belongs to a technical domain. Reading through the subscription in Figure 6-9, the system will understand that the subscriber needs documents that are in the domain of electrical engineering for Project ABC and published after January 1, 2008. In addition, the documents should be published a specific system user and have keyword “wiring”.. Of course, the subscriber can modify the definition of a subscription by clicking on the “Edit Attributes” button to add/remove attributes or change the conditions.
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Figure 6-9: Define Subscription (Populated)
6.5.4 Browse Knowledge Items
As shown in Figure 6-10, the main window of the web page for browsing for a given knowledge item published in the CIKP will include three sections: the top section is for displaying the information about this knowledge item, the middle section is used for social tagging, and the bottom section is used for social commenting. Social voting is incorporated into all three sections.
The top section in Figure 6-10 displays all information about the knowledge item in question. The values of all attributes are read-only if the current system user is not the original publisher of this knowledge item. A system user is not able to edit any attribute of a
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Figure 6-10: Browse Knowledge Items knowledge item published by other users. In next version, we will have a “Flag Attributes” button by which a user could tell the system that some information displayed is wrong if
Chapter 6: CIKP Framework 172 he/she feels that it is. Then the system will pass this notice to the original publisher, who can then take the action needed to rectify it. An “Edit Attributes” button will be showing up at the lower-left corner of this panel if the current system user is the original publisher of this knowledge item and he/she will be able to edit any attribute defined earlier.
The middle section is used to collect social tags. General system users other than the original publisher, will be are able to add additional tags to the knowledge item by pushing the “Add Tag” button. The CIKP system will generate a new row in this area and allow users to drag a valid attribute from the ontology tree and assign a value. In this way, the general public can enrich the description of a published item. In this example, the new tags include the keyword “Code235” (someone indicates that this change order notice is directly related to a specific code Code235), language information “English” (someone wants to clearly say the document is written in English), and the date of creation of this document if the user who added this tag actually know the date of creation. Any system user can vote on any added tag to express their opinion by pushing one of the two buttons: “Agree” and “Disagree”. The number of votes will be displayed on the web page to let users know the social opinion about each tag. If the current system user is the original publisher, the buttons mentioned before for adding tags or voting are greyed out, but the buttons “Approve” and “Reject” will be available. This means that the original publisher is able to approve or reject tags placed by other users. All approved tags will be moved to the top section as a part of the description for the knowledge item, and all rejected tags will remain in the middle section but displayed in a different colour so that other users will know what happened before.
The bottom section in Figure 6-10 is the area where all system users can freely express their opinions by posting comments. This option is open to all system users including the original publisher. For each comment place, the system will show the name of its poster, as well as the date and time that the comment was posted. By clicking on the name of the poster, system
Chapter 6: CIKP Framework 173 users can browse the profile of that poster (see Section 6.5.5). If someone wants to post a new comment, he/she can push the “Add Comments” button and an editing area will show up to allow the user to edit the post. One very important function for the social commenting section is social voting on posted comments. Any system user may agree or disagree with the comments posted by others, and is able to flag any inappropriate comments. In this example, there is on comment posed by user “David” and the content is “This is a test comment.” There is one user who agrees with the comment and zero user who disagrees with the comment.
6.5.5 Browse User Profiles
System users of the CIKP will be able to browse not only for knowledge items but also for profiles of system users. By clicking on any name displayed on the screen, people will see the profile of that system user, as shown in Figure 6-11.
The main window on the web page for browsing user profiles starts with contents similar to that on the page that defines a user’s profile (Figure 6-5 in Section 6.5.1). All information about the user is listed, including the type of actor and role(s), as well as associated attributes. General system users are not able to modify other people’s profiles, but in next version of the CIKP system they can flag attribute that they believe is wrong. The CIKP system will then pass the notice on to the owner of the profile who is then able to act upon it. If the current system user is the owner of the profile, there is an “Edit Attribute” button on the screen from where the user can edit his/her profile.
The next section in Figure 6-11 is used for social tagging. Similar to the function defined in the function of browsing knowledge items (see Section 6.5.4), general system users are able to place tags on the profile being browsed. By clicking on the “Add Tag” button, people can
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Figure 6-11: Browse User Profiles
Chapter 6: CIKP Framework 175 drag any attribute from the ontology tree under the class Individual Attribute and assign a value to that attribute. In this way, the description of a given system user will be semantically enriched. General system users are also able to vote on the tags placed by other users by clicking on one of to buttons: “Agree”, “Disagree”. If the current system user is the owner of the profile (as shown in this example), only two buttons (“Approve” and “Reject”) are available and others will be greyed out. All approved tags will be moved to the user profile definition section, and all rejected tags will remain in a distinguishable colour.
The next section of this page is about the CoPs subscribed to by the user being browsed. In this example, user Peter Chan belongs to three communities: the Community of Building Envelop, the Community of Green Roof, and the Community of Sustainable Design. In next version, we will implement the function of “flag”. If someone feels that Mr. Chan should not belong to one of those CoPs, he/she can click on “Flag Communities” to report the mistake, and again this report will be passed to the owner of this profile. A system user can log in his or her own account and go to personal information panel from there he or she can subscribe or unsubscribe any online communities.
There was a lot of debate in the development of the CIKP framework, especially over the evaluation stage (see Section 7.2), regarding the function of tagging a user. The concerns were raised from the perspective of privacy. Some people may be offended if their profiles are tagged with some information that they do not want to show publically. Currently the technical solution to that problem is such that system users will be able to customize the preference about tags that can be placed on their profiles. They can opt against receiving any social tags, or they can indicate, from a list of attributes available to human beings, which tags are acceptable or not acceptable. Then general system users will be able to select and place tags from those that are granted by the profile owner.
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6.5.6 User Home Page
The user home page is defined as the hub of all system functions: profile management, community management, the management of knowledge items, publishing, subscribing, querying, and browsing. It has been mentioned in Section 6.5.1 that all registered system users will be directed to the web page to define their profiles when they log into the system for the first time. After that, when they log into the system again, they will be directed to their home page, as shown in Figure 6-12.
On the left side of the user home page, there are three areas – one is for user profile and for communities. In the area of user profile, major attributes will be displayed, and a user can click on the “View Full Profile and Edit” button to access the user profile web page (see Figure 6-12) to view the full profile and edit any attribute if desired. In the area of communities, a user can click on the “View” button to see a full list of the members of each community, or click on the “Subscribe/Unsubscribe Communities” button to join or leave any community.
The bottom area on the left is for the display of ontology tree. All concepts in the ontology will be displayed in a hierarchical structure. Function Menu area in the middle on the left includes all major function of the CIKP system Clicking on the “Publish” button will access the publishing function described in Section 6.5.2 (see Figures 6-6 and 6-7), while clicking on the “Subscribe” button will access the subscribing function discussed in Section 6.5.3 (see Figures 6-8 and 6-9). Clicking on the “Query” button will access the querying function that will be discussed in Section 6.5.7 (see Figure 6-13). Clicking on the “Browse Item” button will access the browsing knowledge item function discussed in Section 6.5.4 (see Figure 6-10), and clicking on the “Browse User” button will access the browsing-user-profile function discussed in Section 6.5.5 (see Figure 6-11).
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Figure 6-12: User Home Page
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The main window is used to manage all knowledge items related to the current user and the user profiles that the current user has participated in through social activities. - The first two sections are used to manage all knowledge items and user profiles received by the current system user. These items are supposed to be composed of items that match the subscriptions defined by the user. - Next section is called “Reasoned Item”. If there are some knowledge items that the current user did not explicitly subscribe to by after reasoning the user’s profile And the database of knowledge items, the system believes the user needs some information, then those items will be showing up in this section. - The “Dynamic Matched Item” section is used to display items that are received through dynamic reasoning described in Section 6.4. - The “Published Item” section is used to manage all the knowledge items that the current user has contributed to the system. - The “Participated Item” section is used to manage all knowledge items that the current user has participated in socially. The system gathers those items together to allow a system user to follow up on the items that he/she has expressed opinions about. - The last section is used to manage all user profiles that the current user has been socially involved in by either adding tags or voting on other tags.
For each of these four sections, all entries are listed with a set of header items. The CIKP system will allow users to customize the header items (which will be shown and in what sequence) for each section based on the users’ preferences. Also, system users will be able to sort all items in each section using the criterion associated with any header. For the convenience of displaying all four sections in a limited length on the web page, each section will allow a certain number of entries to be displayed on one page, and the rest will be accessed by turning pages.
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6.5.7 Query the System
The main reason for having the query function is to accommodate the so-called “one time only interest” of some system users. The system will be constantly running to match subscriptions (either indicated by system users or inferred by the ontology CIKP-Onto) with all published knowledge items. It sometimes happens that a system user will have an interest in some knowledge items based on unique conditions, and they do not always want to be informed about these particular kinds of knowledge items. In this case, the querying function will become very handy. For example, a system user may want to find a specific drawing of Project ABC and the user only needs to find it once but does not want to subscribe to any other drawings for this project. In this case, the user can fire a query as shown in Figure 6-13.
Figure 6-13: Query the System
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Similar to the function of defining a subscription, the user needs to first indicate what type of knowledge item he/she is looking for. In the example described, the user has only to drag a concept Drawing from the ontology tree under the class Knowledge Item and place it in the text field. After that, the CIKP system will ask the user to assign values to all mandatory attributes for the concept Drawing, such as Project Affiliation and Technical Domain. Then the user has the ability to add any attribute to further constrain the query, such as the date, author, file type, and file size. The more specific the conditions are, the more precise the returned result will be. If there are multiple knowledge items that meet the query conditions, the user will need to further screen them manually or modify the query conditions to perform another query.
6.6 CIKP FRAMEWORK VS. EXISTING SYSTEMS FOR INFORMATION MANAGEMENT
The CIKP framework differs from those existing systems in three ways: (1) it focuses on dynamic communication among people, (2) it aims at information and knowledge rather than data, and (3) it is able to understand the users and make event matches smarter.
There are six major application areas in information management: project management, project portfolio management, resource management, document management, collaboration, and issue tracking. In the AEC industry, some software applications emphasize one or just a few areas. For example, Microsoft Project covers the areas of project management and resource management. Other software applications attempt to cover all aspects, such as the Primavera Project Planner and Planisware. Almost all existing software applications focus on document management. Some users are primarily responsible for providing information and other users are mainly the information consumers. Communication is very limited and follows the traditional linear model with pre-defined channels. By contrast, the CIKP
Chapter 6: CIKP Framework 181 framework focuses on dynamic communication between people based on their real needs. First, it encourages a culture of sharing through a many-to-many communication model. Second, it harnesses the wisdom of crowds using Social Web concepts. Third, it combines the push mode and pull mode of communication, i.e., the system will push the information and knowledge to users and users can pull the information and knowledge from the system by querying.
Most existing software applications are good at data exchange and some of them are capable of dealing with structured data (information or explicit knowledge), but the CIKP framework attempts to handle tacit knowledge by using ontologies. The reason that most computer systems are able to handle data is that there are standardized data exchange protocols or formats. Ontologies act just like a standardized protocol for exchanging information and knowledge. Using ontologies as an interoperable knowledge representation, the CIKP framework allows users to explicitly formulate their experience or tacit knowledge and thus be able to share it with others.
The CIKP framework is smarter than most existing software applications for two reasons. First, the embedded ontology is able to understand user profiles and thus is able to direct information and knowledge to the right user(s) in an automatic way, even though that information and knowledge are not intentionally required by the user(s). Second, the CIKP system attempts to match publications and subscriptions in a smarter way. Most publish/subscribe systems match publications and subscriptions based on syntax, but the CIKP framework uses ontologies to understand the semantics in publications and subscriptions and can thus realize semantic match.
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7 EVALUATION 7
7.1 EVALUATION OF THE ONTOLOGIES
The first step of ontology evaluation work is to identify the criteria based on which the ontology will be examined. Based on the ontology evaluation methods summarized by Gómez-Pérez et al. (2004) and the evaluation work conducted by El-Diraby et al. (2005) and El-Diraby and Briceno (2005), five evaluation criteria have been selected in this research to evaluate the domain-level ontology, AR-Onto, and the application-level ontology, CIKP-Onto: - Representation. Representation in ontology evaluation means the expressiveness of the model in representing the real world of the subject domain. Representation concerns how detailed an ontology can be in represent things. - Coverage. Coverage in ontology evaluation concerns how complete the model is in representing the real world of the subject domain in terms of the entities involved and relationships associated with them, as well as the necessary axioms.
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- Consistency. Consistency in ontology evaluation refers to whether it is possible to obtain contradictory conclusions from valid definitions. A given definition is consistent if and only if the individual definition is consistent and no contradictory knowledge can be inferred from other definitions and axioms (Gómez-Pérez et al., 2004). - Ease of Use. An easy-to-navigate ontology means that it is not too difficult to locate concepts in the taxonomy hierarchy and relationship structure. This is important for ensuring easy knowledge access, retrieval, reuse, and maintenance in applications that utilize the ontology. - Conciseness. Conciseness in ontology evaluation consists of three elements: (1) the ontology does not store any unnecessary or useless definitions, (2) explicit redundancies between definitions of terms do not exist, and (3) redundancies cannot be inferred from other definitions and axioms.
It has been mentioned in Appendix E that the evaluation of an ontology includes technical evaluation and user evaluation. Technical evaluation is normally performed by the developer(s) in order to be sure that all development requirements have been met or to see whether there has been any violation in ontology definition. Two methods have been employed in this research to conduct technical evaluation: competency question review and automatic consistency check. User evaluation is used to examine an ontology from the perspective of its potential users. Domain-expert interviews have been used in this research as the method for user evaluation.
Table 7-1 summarizes the ontology evaluation criteria and associated evaluation methods that are able to examine them. A competency question review will be able to handle the coverage and representation, automatic consistency check will be able to check the consistency and conciseness, and the domain-expert interview will examine the issues of coverage, representation, conciseness and ease of use.
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Table 7-1: Ontology Evaluation Criteria and Methods Evaluation Criteria Evaluation Tools Consistency Coverage Representation Conciseness Ease of Use Competency √ √ Technical Question Evaluation Automatic Consistency Check √ √
User Expert Interview Evaluation √ √ √ √
7.1.1 Competency Question Review
As has been discussed in Chapters 4 and 5, two types of competency questions have been defined during the development of the AR-Onto and the CIKP-Onto: competency questions to extract domain concepts and relationships and competency questions to validate the ontology. Based on the methodology proposed by Grüninger and Fox (1995), competency questions serve as a frame of reference or requirement specification against which the ontology must be evaluated. As such, those competency questions that are pre-defined specifically for validating the ontology are used to support the evaluation of the ontology’s representation and coverage. Competency question review can be done by either the developer(s) or a third party such as potential users, domain experts, or ontology development experts.
The two ontologies developed in this research (the AR-Onto and the CIKP-Onto) are checked for their conformance to the competency questions. The competency questions that are mainly used for extracting concepts and relationships are answered by higher-level concepts and relationships, and the competency questions that are designed for validating the ontology are normally answered with reference to more specific concepts. For example, there is a hyponymy competency question: “What are the major types of individuals/organizations in
Chapter 7: Evaluation 185 the AEC industry?” This question is mainly used to identify the major types of individual actors and organizational actors, but it is still a valid question in validating the ontology. By looking through the ontology, one can find four types of individual actors in the two ontologies (professional individual, technician or specialist, skilled labourer and unskilled labourer) and two types of organizational actors in the two ontologies (government organization and non-government organization).
After reviewing the competency questions, it has been concluded that the two ontologies are compliant with the scope defined before ontology development. The ability to answer competency questions means that the ontology in question has met the requirements set at the beginning of its development. These requirements define the scope of the ontology in terms of its representation and coverage. Thus, the ability to answer competency questions set by the developer(s) means that the ontology has stratified the representation and coverage defined by the developer(s), but not the representation and coverage from the perspective of the subject domain or potential users. There is still a need to validate the ontology’s representation and coverage from the perspective of potential users.
7.1.2 Automatic Consistency Check
The implementation languages used in the two ontologies provide a technical base for automatic consistency check. As discussed in Chapters 4 and 5, the domain-level ontology, AR-Onto, is coded in OWL DL (DL stands for Description Logics) using Protégé and the application-level ontology CIKP-Onto is coded in OWL Full using Protégé and SWRL. The CIKP-Onto has been extended from its domain-level counterpart, AR-Onto, by tailoring the content (concepts, relationships, and axioms) associated with industrial actors and roles and introducing new content for the needs of modelling communication. In fact, the sublanguage used to edit the CIKP-Onto is still OWL DL until such time as SWRL is involved to enrich
Chapter 7: Evaluation 186 the rules for better serving the application scenarios.
A key motivation for basing the language of the two ontologies on OWL-DL is that DL systems can be processed by a semantic reasoner (known as a reasoning engine or rules engine). A semantic reasoner is piece of software able to infer logical consequences from a set of asserted facts and axioms. Therefore, a DL-based system can be used to provide computational services for ontology tools and applications. The increasing use of ontologies, along with increases in their size and complexity, brings with it a need for efficient DL reasoners. There are several reasoners that are able to reason OWL-DL and handle SWRL rules. Existing popular semantic reasoners include RacerPro, FaCT++, Pellet, Jena, SweetRules, and many others.
One of the main services offered by a semantic reasoner is consistency check. Based on the description (conditions) of a class, the reasoner can check to see whether it is possible for the class to have any instances. A class is deemed to be inconsistent if it cannot possibly have any instances (Horridet et al., 2004). For example, the ontology defines the class Government Organization and the class Non-Government Organization as two disjointed (distinct and separate) classes. Consistency check will return a mistake if there is a class that is coded as a subclass of both Government Organization and Non-Government Organization because there would be no instances for such a class.
Another standard service offered by a reasoner is to test whether or not one class is a subclass of another class by checking the descriptions of the classes. By checking all of the classes in an ontology for a new superclass/subclass relationship, it is possible for a reasoner to compute the inferred ontology class hierarchy which includes both asserted and inferred hyponymy relationships.
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Most existing semantic reasoners are able to perform standard consistency check and taxonomy classification. Pellet (version 1.5.1) was employed in this research. Pellet is an open-source Java-based OWL-DL reasoner which is based on the tableaux algorithms developed for expressive Description Logics. It can be used in conjunction with both Jena and OWL API (Application Programming Interface) libraries and also provides a DIG (DL Implementation Group) interface. There are several reasons why Pellet was selected: - Currently, Pellet is the first and only sound and complete DL reasoner that can handle the full expressivity of OWL DL. It supports the reasoning about nominals (enumerated classes). Therefore, OWL constructs “owl:oneOf” and “owl:hasValue” can be used freely. - It is an open-source reasoner and has been integrated into the Protégé OWL development environment, so that there is no need of extra effort to obtain it. - It has been used in many other ontology development projects and its stability has been demonstrated.
The result of the consistency check indicates that both the AR-Onto and the CIKP-Onto are consistent. In order to prove that the consistency check was working as expected, counterevidence has been demonstrated. As the class Government Organization and the class Non-Government Organization have been defined to be disjointed, a probe class Consistency Test was created and defined to be the subclass of both Government Organization and Non-Government Organization. As shown in Figure 7-1, the class Consistency Test is reddened in the taxonomy structure and Pellet also indicates that there is an inconsistent concept in its report. After this probe class is removed, the consistency check is passed without further concern.
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Figure 7-1: Counterevidence of Ontology Consistency
7.1.3 Expert Evaluation Interviews
In order to evaluate the AR-Onto and the CIKP-Onto from the perspective of potential users, a series of expert interviews has been conducted. Eight industry experts have been visited individually and their responses have been collected and carefully analyzed. This section discusses the evaluation interviews from four points of view: respondent selection, interview structures and data collection, questionnaire design, and result analysis.
Respondent Selection The respondents (interviewees selected to evaluate the ontologies) were selected through a
Chapter 7: Evaluation 189 purpose-based sampling method. As a kind of non-random sampling technique, purpose-based sampling targets a specific pre-defined group of samples (industrial experts with required areas of expertise) when the population for the study is rare, highly individual, difficult to locate, or difficult to recruit. A purpose-based sampling method was used in this research for several reasons: (1) requirements of specific areas of expertise for respondents, (2) requirement of broader industrial representation, and (3) limited time frame.
There are several requirements for respondents. First, in order to be able to provide appropriate and countable responses, selected respondents should be experienced in the AEC industry and have a comprehensive understanding of construction projects. Second, selected respondents should be information- and knowledge-intensive workers so that they know if their needs for information and knowledge are well represented in the ontologies. Third, selected respondents should have a basic understanding of ICT and information systems so that they will quickly understand the nature of this research in terms its objectives, scope, and technical approaches.
The selection of interview respondents should cover major parties in the AEC industry. As the ontologies, especially the domain-level ontology, AR-Onto, are developed for the entire AEC industry, it is therefore necessary to collect responses from a broader than average range of industrial participants. This requirement comes into considerable conflict with the constraint of available time for investigation. There is not enough time to perform a large-scale investigation, which means that the number of interviews must be limited. It has to be well balanced in its selection of a limited number of respondents if it is to represent as many industrial parties as possible.
Combining the requirement of industrial areas of expertise and better coverage, the constraint of limited interviews requires that the choosing of respondents be very selective and
Chapter 7: Evaluation 190 purpose-oriented. Eight industrial experts have been selected to be interviewed at the ontology evaluation stage. Table 7-2 shows their composition in terms of their specialties, experiences, and industrial representation.
Table 7-2: Respondents of Ontology Evaluation Experience Expertise Industrial (Years in Industry) Representation Respondent 1 15 Project Management Owner Respondent 2 30 Construction Contractor Association Services Association Respondent 3 2 Project Management Contractor Information Systems Respondent 4 25 Project Management Project Manager, Construction Private Sector Respondent 5 32 Surveying Mapping GIS Government Information Systems Respondent 6 24 Construction Contractor Consultations Association Respondent 7 22 Engineering Planning Government Respondent 8 9 Transportation Planning Designer, Technical Design Public Sector
Interview Structure and Data Collection The ontology evaluation interviews were conducted in two stages: the pre-interview introduction stage and the actual interview. As soon as the respondents agreed to participate in ontology evaluation interviews, a ten-page research summary was sent to them. The summary introduced, in plain language, the background of this research, proposed deliverables, and technical approaches. The summary focused on the introduction of the concept “ontology” itself. Most people are unfamiliar with the concept of ontology and thus have no idea about its significance and development process. Through reading this summary, the respondents come to understand this research and gain some basic understanding about the concept of ontology. When the date of the interview was set, a one-page agenda was sent to the all respondents to outline for them the procedures for the interview.
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Data were collected through a set of one-to-one interviews which lasted 60-90 minutes. The actual interviews were also divided into parts. - At the beginning, a 30-minute presentation was given to respondents. The objective of this presentation was to refresh the respondents’ understanding of this research and give them more information about the two ontologies. The major contents of the two ontologies (concepts, relationships, and axioms) were presented to the respondents. - In a short Q&A section after the presentation, respondents were free to ask any questions about the research and the ontologies. Also, they were able to briefly go over the questionnaire to make sure they understood their tasks because the investigator (the author) would not be interrupting the respondents when they were filling out their questionnaires. - Respondents then took 30-40 minutes to complete the questionnaire. - There was a short discussion at the end of the interview to collect their comments.
It should be mentioned that the ontology evaluation interview only covers the two major components of the two ontologies: the concepts and the relationships. The axioms are either coded in Protégé-OWL or expressed in SWRL, using FOL (First Order Logic) language. None of the industrial experts being interviewed were familiar with the notation of Protégé-OWL or the FOL, and therefore, it was very hard to effectively evaluate the axioms in the short time allotted for the interview.
Questionnaire Design The ontology evaluation questionnaire for this research consisted of four sections: respondent background, ease of navigation, abstraction and categorization, and overall evaluation. The full evaluation questionnaire is shown in Appendix H.
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Section 1 aimed at collecting basic respondent information and information on their backgrounds in terms of information and knowledge representation. The basic information collected included name, title, organization, years of experience, areas of expertise, and basic contact information. Respondents were also asked to compare their awareness of two concepts (“information and knowledge management” and “ontology”) before and after the presentation. The result will show the effectiveness of the presentation in clarifying the two concepts.
In Section 2, nine actor concepts and four relationships were given. The respondents were asked to locate them in the ontology using the Protégé editor and rate how easy it was to find those concepts. Section 3 provided respondents with seven concepts and their hierarchical path in ontology taxonomy, and respondents were asked to rate how fully they agreed with the categorization of those concepts. Section 4 asked respondents to provide their overall evaluation, as well as any comments they might have.
Interview Results and Data Analysis A Likert six-point scale was used in most questions in the ontology-evaluation questionnaire to record the responses of respondents. A Likert scale is a psychometric scale commonly used in questionnaires where respondents specify their level of agreement with a statement. In this research, a six-point scale was used with the number one lying at one extreme (for example, strongly agree) and six at the other extreme (for example, strongly disagree). There is an argument about whether individual Likert items should be considered as interval-level data or merely ordered-categorical data. Many people regard Likert items only as ordinal data, because one cannot assume that respondents perceive all pairs of adjacent levels as equidistant. This research adopted this approach and thus measured the central tendency of the data by median.
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Table 7-3 shows the analysis of Question 1.4, 1.5, 1.6, and 1.7. It indicates that the awareness of information and knowledge management in the AEC industry is moderately low and many people do not know the concept of ontology. All respondents indicated that, after the presentation, their understanding of both concepts had definitely increased.
Table 7-3: Data Analysis of Question 1.4-1.7 Question R1 R2 R3 R4 R5 R6 R7 R8 Median To what extent you are aware of Information and 6 2 6 1 2 3 3 6 3 Knowledge Management being undertaken at industrial level BEFORE the presentation? To what extent you are aware of Information and 5 2 4 1 2 2 1 3 2 Knowledge Management being undertaken at industrial level AFTER the presentation? To what extent you are aware of Ontology 6 2 6 2 3 3 6 6 4.5 BEFORE the presentation? To what extent you are aware of Ontology 5 2 4 2 2 2 1 2 2 AFTER the presentation?
Table 7-4 shows the analysis of Question 2.1. All concepts were randomly selected from the ontology to be evaluated. The average of the medians from all nine concepts was 1.72, which means that the taxonomy structure was very clear for most selected respondents. It was also noted that the response from different respondents varied widely. For example, R3 showed great difficulty in navigating almost all concepts, but R4 indicated a totally reverse response. The common trend is that younger people who are more familiar with computers and technologies find it easier to navigate through an ontology structure than older people who are less familiar with computer systems. Another possible factor causing navigational problems might have been that the software (Protégé-OWL editor) that the respondents were using is a free software application that is mainly used in academic research and thus is not very user-friendly in operation. We drew a similar conclusion when it came to the navigation of relationships (see Table 7-5).
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Table 7-4: Data Analysis of Question 2.1 Concept R1 R2 R3 R4 R5 R6 R7 R8 Median Construction Engineer 2 1 4 1 1 1 5 1 1 Concrete Technician 4 1 6 1 4 2 6 1 3 Roofing Contracting Company 5 3 6 2 3 2 2 3 3 Project Manager 2 1 6 1 4 1 1 2 1.5 Process Initiator 1 1 6 1 1 1 2 1 1 Physical Product Manufacturer 1 2 6 1 1 2 2 2 2 Knowledge 1 1 6 4 3 1 1 1 1 Family Name 3 1 6 1 3 1 1 1 1 Liability 4 2 6 1 2 1 2 3 2
Table 7-5: Data Analysis of Question 2.2 Relationship R1 R2 R3 R4 R5 R6 R7 R8 Median play_Role 2 2 6 4 2 1 3 2 2 has_Experience 2 1 6 2 3 1 2 2 2 has_Email_Address 2 1 6 2 1 1 1 1 1 has_Project_Closing_Date 2 2 6 2 1 1 1 1 1.5
Table 7-6 shows the analysis of Question 3.1, whose purpose was to investigate the abstraction and categorization of concepts. All concepts were randomly selected from the ontology to be evaluated. The average of medians for seven concepts and their hierarchical paths is 1.79. This value indicates that the categorization of these concepts is agreed upon by most industrial experts. There was no significant variance between respondents.
Questions 4.1, 4.2, 4.3, and 4.4 were used to collect the overall evaluation of the ontologies in terms of coverage, representation, and ease of use (Table 7-7). The answers to Question 4.1 basically reflected the results of Question 2.1 and 2.2. Some people thought that navigation was easy, while others found it difficult. Question 4.2 concerned the representation of the ontology. The median value of the answers was 2.5, which means that the selected respondents were moderately familiar with the concepts coded in the ontologies. This indicates that the ontologies were adequately representative of a variety of application
Chapter 7: Evaluation 195 fields because the selected respondents were from widely differing areas of activity, e.g., project owners, government managers, contractors, designers, and so on. For Questions 4.3 and 4.4, most respondents felt quite comfortable in saying that the ontologies covered the majority of concepts and relationships associated with industrial actors and roles, after they had carefully navigated through the ontologies. However, some respondents felt that they would have needed more time to investigate those concepts and relationships in order to be able to properly answer those two questions. These cases have been indicated with a hyphen in Table 7-7.
Table 7-6: Data Analysis of Question 3.1 Concept R1 R2 R3 R4 R5 R6 R7 R8 Median Entity Æ Actor Æ Individual Actor Æ Professional Individual Æ Engineering 2 1 1 1 1 1 2 1 1 Professional Æ Civil Engineer Æ Structural Engineer Entity Æ Actor Æ Individual Actor Æ Skilled Individual Æ Skilled Individual in Natural and 3 1 2 1 1 1 4 1 1 Applied Sciences Æ Architectural Technologist and Technician Æ Architectural Technician Entity Æ Actor Æ Organization Actor Æ Non Government Organization Æ Organization in Professional Scientific Technical Services 2 2 2 1 3 1 1 1 1.5 Industry Æ Architectural Engineering and Related Services Company Æ Engineering Services Company Entity Æ Role Æ Role of Project Æ Project 2 2 1 1 2 1 3 2 2 Contractor Æ Project General Contractor Entity Attribute Æ Actor Attribute Æ Human Attribute ÆHuman Abstract Attribute Æ 2 1 2 2 2 1 3 2 2 Education Background Entity Attribute Æ Organization Attribute Æ Organization Physical Attribute Æ Organization 3 3 2 3 3 1 4 2 3 Affiliation Æ Project Affiliation Entity Attribute Æ Role Attribute Æ 2 2 1 2 2 1 1 2 2 Responsibility
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Table 7-7: Data Analysis of Question 4.1-4.4 Question R1 R2 R3 R4 R5 R6 R7 R8 Median How easy was it to navigate through the 4 3 6 2 4 2 2 2 2.5 concepts and relationships? How familiar are you with the concepts and 4 3 6 3 1 2 2 2 2.5 relationships used in this ontology? Overall, does the ontology cover the main actors, roles and their attributes in the AEC - 2 2 3 2 1 1 2 2 industry? Overall, does the ontology cover the major relationship between actors/roles and other - 2 2 - 2 1 2 2 2 entities in the AEC industry
The answers to Question 4.5 provided more commentary on the ontologies and the evaluation work. One commonly expressed opinion was that a respondent would need more time to properly look through the ontologies. Everyone agreed that this is an important and significant aspect of the work involved in explicitly representing domain information and knowledge, but that a better evaluation result could be achieved if there were enough people and sufficient time to comb through each concept and relationship. Another issue raised from the interviews was that different people will have different understandings in categorizing concepts because they have slightly different work environments and knowledge backgrounds. This is exactly why the industry needs a commonly agreed-upon knowledge representation – an ontology – but there can be no one single ontology that satisfies everyone in the industry. The domain-level ontology will take the understanding of the majority into account, and the rest has to follow an officially defined structure.
7.2 EVALUATION OF THE CIKP FRAMEWORK
As with the evaluation work conducted for the ontologies, four criteria were first identified for evaluating the CIKP framework:
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- Requirement conformity. A set of service scenarios was defined after the requirement analysis for the CIKP framework was completed, and there was a need to review those scenarios and designed functions to see whether or not the requirements for fulfilling those services had been met. - Functionality. In order to realize the service goal defined in the development of CIKP framework, a series of functions were specified (see Section 6.3). Those functions needed to be validated and verified during the course of the evaluation. - Industry recognition. Because the intended users of the CIKP system are industrial participants, it proved to be of vital importance to have industrial people evaluate the CIKP framework in terms of its services and functions. - Ease of use. User-friendliness and ease of use are among the top criteria in the development of many computer systems. Taking into the consideration that the AEC industry has lower ICT recognition than other industries, ease of use is more important in the development of the CIKP framework.
The evaluation of the CIKP framework should also include both technical evaluation and user evaluation. Technical evaluation included requirement-conformity assessment and automatic reasoning. User evaluation was conducted by a focus group study. Table 7-8 summarizes the CIKP framework evaluation criteria and associated evaluation methods that are able to examine them. In our research, an assessment was made to check for requirement conformity, and automatic reasoning was used to check for functionality, while focus group study was used to check the requirement conformity, industry recognition, and ease of use.
It should be noted that, for the development of any computer systems, a prototype application is an important method for evaluating the work. This research recognizes this point and a prototype software application is currently under development. The actual software development work started in January 2009 and will be finished in July 2009. As soon as a
Chapter 7: Evaluation 198 workable CIKP system is up and running, systematic testing will be conducted and the CIKP framework will be further evaluated in terms of requirement conformity, functionality, and ease of use.
Table 7-8: CIKP Framework Evaluation Criteria and Methods Evaluation Criteria Evaluation Tools Requirement Industry Functionality Ease of Use Conformity Recognition Conformity √ Technical Assessment Evaluation Automatic Reasoning √ User Focus Group Evaluation Study √ √ √
7.2.1 Requirement Conformity Assessment
The requirement analysis conducted at the beginning of the development of the CIKP framework identified service goals and a set of service scenarios for the CIKP system (see Section 6.1.2). Those defined service goals and scenarios served as a frame of reference and as requirement specifications against which the CIKP framework could be evaluated. The requirement conformity assessment was carried out by the developer (the author). All designed functions and associated activities were carefully examined against the services defined in the requirement analysis. The results of the assessment confirmed that the functions designed in the CIKP framework conformed to the requirements of services, although this could only be a qualitative assessment.
7.2.2 Automatic Reasoning
The application-level ontology, CIKP-Onto, is the key component in the CIKP framework which is designed to support the semantics in matching publications and subscriptions. It has
Chapter 7: Evaluation 199 been said in Section 7.1.2 that the CIKP-Onto is coded in OWL DL until the SWRL rules are introduced to accommodate complex rules for coordinating information and knowledge flows. The consistency of the two ontologies has been checked by a built-in reasoner, Pellet (see Section 7.1.2), and this section will address the automatic reasoning for the rules coded in SWRL.
As indicated in Section 5.5.2, OWL DL is limited in expressing complex rules. Protégé-OWL is able to handle simple axioms such as the constraints on cardinality or constraints on range/domain for a given relationship. However it is not capable of expressing complex rules such as the example given in Section 5.5.2. SWRL was used to handle complex rules by combining the OWL DL and OWL Lite sublanguages with the Unary/Binary Datalog RuleML sublanguages of the Rule Markup Language. SWRL rules were first written in natural language and then coded in the Protégé SWRLTab.
In order to ensure that the CIKP system works as designed, there is a need to verify those rules written in SWRL. There are several semantic reasoners that support rule reasoning. Some of them support only their proprietary rule formats such as Jena and OWLIM, while some support (fully or partially) SWRL rules such as Hoolet, Pellet, KAON2, Jess, RacerPro and SweetRules. Very few support both SWRL and their own rule formats such as Bossam. Although Pellet has been used to perform consistency checks and claims to support SWRL rules, Jess has been selected in this research to handle SWRL rules for two reasons: - Some reports indicate that Pellet still has some problem in dealing with SWRL rules. Those reports have been showing up on the Web for a while, although there is no formal report in published literatures. - Jess is currently the most mature SWRL engine and has been implemented in many knowledge-based systems.
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Jess is not an open-licence product, but a free licence (Version 7.1.7) has been obtained by this research team for academic use only. The Jess engine was then integrated into a Protégé-OWL development environment to reason rules coded in SWRL. Each rule coded in SWRL was carefully verified by the Jess reasoning engine, and several mistakes were detected and rectified. An example of SWRL rule verification is provided in Appendix J.
7.2.3 Focus Group Study
A focus group provides a kind of forum for a kind of qualitative research in which a group of people are asked about their attitudes towards a product, service, concept, advertisement, idea, or a kind of packaging. Questions are asked in an interactive group setting where participants are free to talk with other group members. Focus groups are commonly used for evaluating usability of new products or prototypes (Edmunds 1999).
The focus group approach was selected to evaluate the CIKP framework for several reasons that Kontio et al. (2004) have pointed out, as a result of their use of focus groups in the context of software engineering. First, it is a fast and cost-effective method to collect user feedback. Second, it can collect content-rich, qualitative feedback and insights that are difficult or expensive to capture using other research methods. Third, it helps to identify some of the in-depth motivations behind what group members think, unlike questionnaire results, which usually do not provide any particular reasons for an answer.
Focus Group Preparation The purpose of this focus group study was to evaluate three aspects of the CIKP framework. The main objective was to examine the services the CIKP system can provide and the functions through which the services are realized. Another objective was to investigate industry recognition of such an information system in facilitating information exchange and
Chapter 7: Evaluation 201 knowledge sharing. The third objective was to study user interface design in terms of user-friendliness.
All industrial experts who had participated in the ontology evaluation interviews were invited to the focus group study. A one-page introduction was sent to all participants to inform them about the agenda and provide them with logistical information. As a complementary approach, a questionnaire was prepared to collect some quantitative feedback. The two-hour session was held on March 22, 2009 at the Centre for Information Systems in Infrastructure and Construction located in the Department of Civil Engineering at the University of Toronto. The selected room was peaceful and quiet and all participants were seated around a large circular table. The physical setting and environment were arranged to encourage communication and interaction.
Focus Group Execution The session started with an introduction of all focus group participants, followed by a brief agenda rundown to refresh the group’s understanding of the session’s arrangements. The session facilitator (the author) then gave a 30-minute presentation to introduce the CIKP framework. The following aspects were covered: the goal of communication in the AEC industry, existing problems, three application scenarios, technical approaches, and proposed functions for the CIKP system. The focus was established with the introduction of functions. The design of ten key web pages was shown to the group. Each function along with key activities was introduced in full detail.
Following the presentation came a 45-minute free discussion period. Focus group participants asked the facilitator to clarify questions raised in the presentation. Group members talked to each other and the facilitator to discuss the services and functions that the CIKP system provides. All comments and feedback were collected and analyzed, as listed at
Chapter 7: Evaluation 202 the end of this section.
All focus group participants filled out a questionnaire after the discussion. The questionnaire took about 20-30 minutes. At the end, there was a wrap-up discussion before the session was officially ended.
Questionnaire Design There were four sections in the CIKP framework evaluation questionnaire: respondent information, industry recognition and service scenarios, requirement conformity, and overall evaluation. The full evaluation questionnaire is provided in Appendix I.
Section 1 aimed at collecting respondent information and their experiences in using information- management systems. Besides the basic information asked for (similar to that asked for on the ontology-evaluation questionnaire), respondents were asked to provide information about their experience with other information-management systems and their willingness to share information.
In Section 2, a set of descriptive questions were asked to investigate the existing industrial practices in information and knowledge management, as well as their perceptions of the proposed CIKP system. Section 3 provided a set of questions, using a Likert six-point scale, to examine the conformity of designed function to proposed services. Section 4 collected the overall assessment of the CIKP framework, as well as further comments.
Questionnaire Data Analysis Table 7-9 shows the analysis of Question 1.4 and 1.6. The results indicate that not many people are aware of the application of information and knowledge management systems in the AEC industry, especially about their supporting technologies. The answers to Question
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1.5 indicate that about 30% respondents have used information and knowledge management systems such as MS-Project, but most people have not used any system to collectively manage information and knowledge. The answers to Question 1.6 indicate that attitudes towards the sharing of information and knowledge with anonymous people varies a lot among respondents – younger people normally have no problem with this or at least have less concern about it than older people.
Table 7-9: Data Analysis of Question 1.4-1.6 Question Median To what extent you are aware of information and knowledge management software in terms of its 2 functionality and supporting technologies? Have you ever used any information and knowledge management software, for example project - information management software or documentation system used in your organization? How comfortable are you if you share information and knowledge with a group of related but 3 anonymous people?
The answers to Question 2.1 and 2.2 indicate that traditional methods still dominate in people’s approaches to communication. These approaches include phone calls, email, face-to-face meetings, seminars, letters and reports. Some respondents did mention social interaction when they look for information and knowledge, such as consultation with industry experts or networking with peers.
The answers to Question 2.3 indicate that some respondents agree with the findings of this research, i.e., that the information and knowledge obtained from these traditional approaches is limited. Some respondents felt that traditional approaches worked reasonably well for them and that there was too much unfiltered online contents on the Web, so that their way of looking for information and knowledge was networking with their peers.
The answers to Questions 2.4 and 2.5 indicate that the lack of a formal method for managing information and knowledge is a general fact for most organizations in the AEC industry. The
Chapter 7: Evaluation 204 only approach reported in the questionnaire is document management. Thus, most respondents saw the potential of the CIKP system, and perceived the services provided by it as very important to their organizations.
Table 7-10: Data Analysis of Question 3.1-3.7 Question Median Based on the three service scenarios identified, how well do the functions provided by the CIKP 2 framework meet the requirements? Based on the three service scenarios identified, how necessary is the function of defining/editing 1 user profiles to the CIKP framework? Based on the three service scenarios identified, how necessary is the function of publishing (and 1 subscribing to) information and knowledge to the CIKP framework? Based on the three service scenarios identified, how necessary is the function of browsing 1 published items to the CIKP framework? Based on the three service scenarios identified, how necessary is the function of browsing profiles 3 of other system users to the CIKP framework? Based on the three service scenarios identified, how necessary is the function of query to the 1 CIKP framework? Based on the three service scenarios identified, how necessary is the function of social 3 involvement (commenting, tagging, rating, etc.) to the CIKP framework?
The answers to the seven questions in Section 3 (Table 7-10) indicate that most functions are necessary for fulfilling the services proposed. The following functions showed a score of 1 which means that they are absolutely necessary to the CIKP system: defining/editing user profile, publishing (and subscribing to) information and knowledge, browsing published items, and querying the system for information and knowledge. The issue of browsing user profiles evoked debate among respondents. Similarly, respondents also differed in their recognition/acceptance of social involvement. Some people were somewhat concerned about the possible results of social involvement in a fully open system.
Table 7-11 shows the analysis of Question 4.1-4.5. Overall, the questionnaire respondents felt that the system was moderately easy to use and user friendly. They agreed that they could
Chapter 7: Evaluation 205 reasonably describe the information and knowledge to be shared and express their interests in items to be subscribed to. Most respondents agreed that social involvement was a good approach to achieving collective intelligence. The answers to Questions 4.6 will be discussed at the end of this section along with the comments and feedback collected during the discussion period.
Table 7-11: Data Analysis of Question 4.1-4.5 Question Median How friendly was the user interface of the CIKP, compare to other information systems you have 3 used? How easy would it be, if you are using the functions provided by the CIKP framework to 2 exchange information or share knowledge? Overall, how do you agree that you can completely describe the information or knowledge that 3 you want to share with their real semantics? Overall, how do you agree that you can freely express your opinion on published items (about 3 information and knowledge) and profiles of other users? Overall, how do you agree the importance of social involvement in achieve collective 2 intelligence?
Comments and Feedback from the Discussion Period The motivation for general industry participants to use the CIKP system was discussed. Many focus group participants emphasized that industrial people still treat information and knowledge as a big private asset that they are unwilling to share with others. The CIKP system was perceived as a good system that would generate values in the long run for the entire industry. However, it was generally felt that the private parties involved would not see the system as having any particular short-range value to their organizations. Therefore, focus group participants doubted that private parties would buy into this system. The recommendation was to have public parties such as municipalities enforce the use of such an information and knowledge management system in the projects that they manage and in the organizations they control. Also, it was felt that in order to have the system accepted by the private companies, its value would have to be demonstrated to them.
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One possible way of incorporating the CIKP into existing work processes was discussed. Focus group participants indicated that many organizations have their own particular work processes and many industrial people have already been used to their routine work procedures for coordinating information. People will not want to do the same thing twice, for example, people first publish/subscribe something using the CIKP system and then work with already established routine work processes for the same thing. There is a need to customize the CIKP system, and tailor it to individual organization’s work processes.
One issue that was discussed relates to the actual usefulness of the CIKP system. It is agreed that the amount of information and knowledge stored in the system is the key indicator. The more information and knowledge items the system has, the more useful the system is for people in industry. People will not be attracted to use the system if it does not contain enough resources. Therefore, the CIKP system must have a mechanism for attracting people to contribute to the system.
The wording in the user interface was also commented upon. It was suggested that the wording should be formulated from the perspective of general industrial people, and not from that of the experts in ontology development or information system design. The system user should be able to understand user interface intuitively. For example, when the system asks the user to define his/her type of actor, he/she is instructed to “please define an actor for you from the ontology.” General system users who are not familiar with ontology structure will not know what the system wants them do. It would be better if the wording were changed to: “Please let me know what your job/profession is.” Some questions and instructions in the user interface are too general and will confuse users. Therefore, they need to be reformulated from the user’s perspective.
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A security issue was raised and discussed. Every information and knowledge item should have security clearance – some are open to the public, but others may be only shared within a group of pre-defined users. A question is raised: How can the system be designed to allow for the creation of flexible groups with limited access to certain knowledge items?
Some other technical issues were raised in the course of the discussion. Some focus group participants suggested that only the relevant part of the ontology be shown when users need to select something from the taxonomy structure, because the full hierarchical tree tends to overwhelm users in finding proper/valid concepts. Some participants mentioned that the system should have a way of preventing subscribers from receiving multiple copies of the same knowledge item. For example, a given user may define several subscriptions, and there is a certain knowledge item that matches more than one subscription. Would the user then receive multiple copies or only one copy? Some people were concerned about the social voting function. They suggested having a way to differentiate between the votes from experts on the subject in question and the votes from general users. The function of rating a knowledge item would be possible if weighted rates could be computed. Some people also suggested that the sequence of comments should be sortable by different criteria such as posting time or number of positive votes, and by default, the best (or most meaningful) comments should be shown at the top of the list.
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8 CONCLUSIONS AND RECOMMENDATIONS 8
his thesis presents a research project that developed an information system framework Tfor coordinating information and knowledge flow in the AEC industry by integrating a publish/subscribe system, Semantic Web technology, and Social Web concepts. The main premise of this research is that the effective communication of information and knowledge is a promising approach in the AEC industry for improving overall industry performance. It has been widely agreed that, due to the high level of fragmentation that characterizes this industry, inaccurate and untimely information exchange amongst industry participants has caused costly delays and has had even more serious consequences. Meanwhile, because of the industry’s dynamic nature, the knowledge and experiences acquired in the course of individual projects are not shared in a way that favours reuse.
Several industry surveys (Carrillo et al., 2004; Earl, 2001) and the feedback collected at the evaluation stage of this research project have shown that traditional approaches (for example,
208 209 phone calls, emails, meetings, etc.) still dominate communication in the AEC industry and that most existing knowledge-management practices focus only on document management or knowledge classification (Lin et al., 2005) but not on knowledge sharing. Therefore, there is a definite need to have an information and knowledge management system that will harness advanced ICT to effectively coordinate information exchange and knowledge sharing.
Figure 8-1: Problems, Goal, and Solution
Figure 8-1 shows the big picture of this research. Existing problems are listed on the left: fragmentation and dynamics cause limited interorganizational communication, and the lack of interoperable knowledge representation results in the domination of linear communication. Adding both, the industry lacks of a sharing culture. The objectives in communicating information and knowledge in our industry are creating a sharing culture, sharing not only data but also information and knowledge, fostering a many-to-many people-to-people communication paradigm, and linking people with not only information and knowledge but also other people. In order to achieve those objectives, three IT technologies were
Chapter 8: Conclusion and Recommendations 210 incorporated: a publish/subscribe system, Semantic Web technology, and Social Web concepts.
8.1 PROPOSED SOLUTION
Publish/subscribe is an appropriate communication paradigm for handling a highly fragmented industry such as construction. Its decoupling feature fits the fragmented AEC industry very well because it is nearly impossible to synchronize time and space among industry participants just for knowledge management purposes due to their high mobility and competitive characteristics. Publish/subscribe systems can be deployed at any level and have the potential to break down the boundaries between organizations. This many-to-many, people-to-people communication paradigm, as the complementary approach to traditional linear communication, encourages a culture of sharing, because each participating entity shares with the whole and learns/benefits from the whole.
Although publish/subscribe systems are a good solution for communicating information and knowledge in the AEC industry, there is still room to improve their performance. In order to enrich the expressiveness of events, Semantic Web technology is incorporated by developing ontologies as a formal and interoperable knowledge representation. Generally speaking, the establishment of a set of interoperable ontologies to provide common ground in this industry can make it possible for tacit knowledge to be explicitly handled and for different systems to understand each other, given that they are based on common ontologies. With the help of ontologies, publish/subscribe systems will be able to perform smarter matching because of the semantic expression of events. Ontologies can also help users in formulating subscriptions when users are not sure what to subscribe to. This is geared to handling unknown unknowns.
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Social Web concepts are introduced into publish/subscribe systems to enrich the description of events. Social involvement breaks down linear communication and encourages the paradigm of many-to-many, people-to-people communication. Each published item can be socially reviewed, tagged, flagged, and commented on by numerous peer users, so that any missing information can be added, any wrong information can be corrected, and any malicious information can be removed. This will effectively enrich the quality of expression of any published events and thus improve the Quality of Service (QoS).
8.2 RESEARCH OUTCOMES
The first outcome has been a domain-level ontology (AR-Onto) to encapsulate industry knowledge about actors, roles, and their attributes in the AEC industry. The philosophical foundation of actors and roles has been studied and an actor-role model has been developed to semantically and dynamically map actors and their roles. The taxonomy has focused on four classes: Actor, Role, Actor Attribute, and Role Attribute. The taxonomy has explicitly defined hyponymy (is-a) relationships between concepts. The cross-tree relationships in the AR-Onto focus on the descriptive and behavioural relationships that are required for the domain level. For the axioms, only the axioms that are valid at the domain level are included. The minimization of domain-level axioms provides maximum flexibility for application-level ontologies.
The second outcome has been an application-level ontology (CIKP-Onto) which has been extended from the AR-Onto by adding more concepts, relationships, and axioms. The CIKP-Onto has been used to support the proposed CIKP system, therefore it focuses more on application requirements. The mechanism for technical communication has been studied and the concepts that are required to describe the process of communicating information and
Chapter 8: Conclusion and Recommendations 212 knowledge have been incorporated into the CIKP-Onto. More relationships have been included to describe a knowledge item. This application-level ontology has also included the relationships required to formulate axioms and rules for inferring the information and knowledge needs of actors and roles. Axioms and rules in the CIKP-Onto emphasize the inference of the default needs of important actors and roles for information and knowledge.
The third outcome has been the CIKP framework, which emphasizes the services and functions provided by the CIKP system. The system serves three purposes: the exchange of project-related information, the sharing of domain-specific knowledge, and the linking of industrial people. Each user has access to some basic functions: - Define (or edit) personal profile. System users can maintain their profiles in the system. In this way, the CIKP will know who a user is and what a user does, and thus be able to behave in a semantic way. - Publish (or subscribe to) information and knowledge. System users are able to share any information and knowledge by publishing it into the system, or they can subscribe to any interested items through formulating a subscription. - Browse published items or profiles of users. System users are able to browse any published items, as well as profiles of other system users. They can socially interact with those items and profiles by placing tags, posting comments, or voting on tags or comments placed by others. - Query the system. System users are able to actively query the system for any interested item that is not formulated in their subscriptions. - Manage Communities. System users are able to create/join/leave any communities of practice. In this way, people with similar interests will be socially linked together.
All three outcomes were evaluated using a variety of methods. The two ontologies were technically assessed by the developer through competency question review and automatic
Chapter 8: Conclusion and Recommendations 213 consistency check. They were also evaluated by a series of interviews with industry experts. The CIKP framework was evaluated by the developer by means of requirement conformity assessment and automatic reasoning. A focus group study was conducted to evaluate the framework from the perspective of potential users. The evaluation results have indicated positive potentials for the two ontologies and the CIKP framework has been viewed favourably as a way of solving communication problems in the AEC industry.
8.3 RECOMMENDATIONS FOR FUTURE WORK
This section discusses a set of recommendations for future research work on ontology development and the implementation of the CIKP system. Some issues were raised or noticed during the course of this research, but they were either beyond its scope or could not be properly and fully dealt with due to the limited time frame allowed. They will be mentioned here just to show that there is room for future work.
8.3.1 Recommendations Related to Ontology Development
Since ontology development is an iterative process that goes in cycles to improve its quality, there is definitely a lot of room for making the ontology better model the real world. Therefore, the findings from this present ontology evaluation should be viewed as the starting point for the future development cycle of the ontology. The modified ontology should be evaluated in a broader range to collect more responses as the reference for the next cycle. The more cycles the development process goes through, the better reflection on reality the ontology will have. This research is just a beginning in the development of a domain-level ontology and an application-level ontology, and the process could be lengthy.
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The domain-level ontology AR-Onto could be extended to expand its level of granularity and cover more concepts and relationships. Currently the AR-Onto has covered all higher levels of individual actors, organizational actors, and roles. Therefore there is a very slim opportunity to extend it to cover a broader scope on actors and roles. However, it is still possible to further cover more specific levels in each category of concepts. The primary emphasis could be that concepts that lie outside of the AEC industry but are closely related to this industry. They could be required by other applications in the future, although they are not the focus of current AR-Onto and CIKP-Onto. Similarly, relationships could be enriched in the AR-Onto to provide more flexibility in modelling the AEC industry. As the concepts and relationships in a domain-level ontology are all building blocks in developing application-level ontologies, there is no harm in having greater detail in the domain-level ontology.
It is also worthwhile to consider the development of other application-level ontologies. The CIKP-Onto has been developed for communicating information and knowledge within the general setting of the AEC industry, and may not be suitable for some specific applications. For example, one may want to have an application-level ontology dedicated to coordinating design collaboration. This would need to model many specific relationships in that field, and many new concepts would also be required to describe those subject-specific activities. Having a series of application-level ontologies should make interoperability a better reality in the AEC industry.
It is also necessary to annotate the ontology in greater detail. Currently, only about one- third of the concepts in the AR-Onto have annotation about their definition and source. It would be more helpful in applying an ontology if all its concepts and relationships were well annotated. One major advantage of using an ontology in an application is to regulate the use of concepts within a setting that may have heterogeneous understanding of them. It would be useful for
Chapter 8: Conclusion and Recommendations 215 users of an application system to know the “official” meaning of a selected concept if the system could read the definition of each concept from the ontology and display it to the user. This kind of annotation is also applicable to relationships. Some other useful annotations would include source, version, and so on.
There is a need to benchmark the evaluation of ontologies. Currently, there is no standard or systematic approach to evaluating ontologies. There are technical approaches to evaluating whether an ontology is correctly coded after it is formulated. Semantic reasoners have been widely used to check for consistency, reclassify concept taxonomy, and validate axioms and rules. This is just to ensure the accuracy of ontology coding, but there is currently no good approach to ensuring that the formulation of the ontology is correct, and that it fairly represents the real world within the scope defined. Many research works have taken qualitative approaches such as self-assessment or interviews with domain experts. Researchers need to explore more measurable ways of evaluating ontologies in this regard.
8.3.2 Recommendations Related to the CIKP System
Our first recommendation for the CIKP system is that it be made to allow system users to modify the ontology structure. One great feature that the CIKP system offers is the social involvement it elicits when describing knowledge items and user profiles. Currently, the system only allows users to drag whatever concepts may be available in the ontology tree to further enrich the description of an item or a profile. System users cannot use their own words in this regard. Many social websites (such as Flickr) give their users rights in selecting words for tags. Contrary to the use of a pre-defined formal taxonomy, system users are building their own vocabulary, which is called a folksonomy. The advantage of using a folksonomy instead of a formal taxonomy is that it gives system users true freedom to express their opinions.
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The reason that the CIKP framework has adopted a fixed taxonomy is twofold. First, in an engineering domain such as construction, there is not too much freedom for the expression of thoughts. It looks self-contradictory if it allows users to use unregulated words in a system that uses ontologies to regulate the use of words. Second, there is not enough time or resources to investigate the technical feasibility of accommodating a user-changeable ontology. Currently, system users can interactively create instances using the given ontology at the front end of the system, and those instances will be populated into the OWL file. However, it may or may not be possible to interactively modify the ontology while the system is running. However, this does not mean that the ontology provided by the system is perfect or that users are not qualified to help the system to improve its ontology. In the future, the system could allow users to suggest new concepts or relationships when placing tags, and periodically the system administrator would then be able to collect those suggested items and send them on to the ontology developer. Concepts and relationships suggested by system users would then be incorporated into the ontology, providing that they are deemed to be reasonable and useful.
Another recommendation is that ratings be provided on the quality of knowledge items. As it stands now, system users are able to vote on each comment or tag placed by other users. It would be advantageous to be able to vote on the knowledge item itself and provide ratings as a reference for system users. However, the problem is that it is hard to differentiate the quality of votes, one from another. It does not make too much sense if the rating is simply computed by counting the number of positive votes and the number of negative votes, because everyone has the right to vote on any item and each vote is counted equally. However, in evaluating a piece of knowledge, a positive vote from a domain expert is much more important or meaningful than a positive vote from a novice, and a vote from a layman is almost meaningless. Currently, the system is not able to identify the creditability of each
Chapter 8: Conclusion and Recommendations 217 user in a given domain or on a given topic. In the future, the CIKP system could incorporate within it a mechanism to assign different levels to system users in each subject domain, and then the vote could be more accurately weighted. In that way, a weighted rating could be computed and provided to the knowledge items. The same mechanism could also be used to rate the quality of comments, and the best or the most useful comments would be shown at the top of the list and system users would see them first when they browse a certain knowledge item.
A third recommendation is that users be provided with more freedom in posting comments. Currently, the CIKP system only allows for a simple post where a user can place a comment. There is no way to reply to comments posted by other users. It would be a nice feature if the comment function could adopt a forum style where system users could comment on other users’ comments. The possible drawback of this forum style is that it could lead to some debate and argument. However, this, in turn, would lead potential users into considerably more detailed discussions on any one single issue. It would provide a chance for system users to clarify the reason(s) why they “agree” or “disagree” with a comment. This forum style function would definitely add value to published knowledge items.
Some other recommendations about the CIKP system have been mentioned in the analysis of comments and feedback collected in focus group studies. For example, a security clearance could be assigned to each knowledge item for better protecting sensitive information and knowledge. The CIKP system should be well integrated with the business processes that are currently used by industry participants. Then, if it is to be effective and viable, it would be necessary to study the policies and regulations that would apply to the use of the system in order to ensure that there is enough information and knowledge available to attract potential users (see Section 7.2.3 for more details.)
Chapter 8: Conclusion and Recommendations
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10 APPENDIX A KNOWLEDGE MANAGEMENT AND LEARNING ORGANIZATION
A.1 Learning Organizations The current interest in learning organization stems from the often frustrating aspects of many other management initiatives. In order to improve the overall performance of organizations, many techniques have been proposed such as Total Quality Management, Business Process Reengineering, and Constructability Analysis. However, the success of these programs is highly dependent upon human factors, such as the skills, experiences, attitudes, and organizational cultures of the people involved. Without the capability of adapting dynamically to changing situations, many management programs do not work in the way that we might have hoped for. A way to be adaptive to dynamic environments is to become learning organizations by encouraging and facilitating organizational learning in all participating members at all levels.
The concept of Learning Organization gained broad recognition after the book The Fifth Discipline was published by its author Peter Senge (1990), who is considered by most other researchers to be the founder of this concept. Many researchers have defined learning organization from different perspectives (Pedlar et al., 1991; Hayes et al., 1988; Slater and Never, 1995; Lessen, 1990). Some authors such as Senge (1990) adopt a broader approach, put all of the other perspectives together and tend to suggest a composite theoretical ideal: “Organizations where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning to learn together.”
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Although Senge’s definition is comprehensive enough, it is somewhat too theoretical and vague to implement in practice. For the concept that describes a strategy with a broad meaning such as “learning organization”, it is better to define it by a set of principles. Three major principles are commonly agreed upon by most researchers and consist of a learning culture, management processes, and implementation tools and techniques. - A learning culture: A learning organization needs to have an organizational climate that nurtures learning. - Management processes: A learning organization needs to have a set of management processes aimed at organizational learning. - Tools and techniques: Organizational learning needs a set of tools and techniques to support individual and group learning.
While the individual members of a group must build new skills and knowledge in order for organizational learning to take place, learning at the organizational level is more than simply the sum of what each individual member knows or can do. An organization is a system that structures, stores, and influences what and how its members learn (Fiol and Lyles, 1985; Hedberg, 1981; Shrivastava, 1983). As such, it possesses a “memory” greater than that of any individual member: “Members (of an organization) come and go, and leadership changes, but organizations’ memories preserve certain behaviours, mental maps, norms and values over time” (Easterby-Smith et al., 2000). This “organizational memory” enables an organization to utilize the capabilities of individual members to achieve group goals while reducing its dependency on any one person. When knowledge is organizational, a group has captured new or expanded capabilities in such a way that it does not depend on particular individuals to exploit them.
A.2 Knowledge Management With the current trend towards replacing the traditional industrial workforce with
Appendix A: Knowledge Management and Learning Organization 237
well-educated, creative, and self-motivated employees, there is an increasing transformation of organizations into knowledge-aware and knowledge-intensive organizations. Knowledge is now considered the most strategically important resource of organizations, and learning is the most strategically important capability for business organizations (Zack, 1999).
The definition of knowledge has been progressing along with the development of philosophical theories. After the first definition by philosophers, a number of new definitions of knowledge were formulated (Gettier, 1963). Despite the argument among philosophers, people in the applied science domain have a commonly agreed-upon understanding about knowledge that includes the “know-why, know-how, and know-who,” – an intangible economic resource deriving from torture revenue (Rennie, 1999). Knowledge is the product of learning which is personal to an individual (Orange et al., 2000).
There are two types of knowledge: explicit knowledge and tacit knowledge. Explicit knowledge is codified and consists of structured knowledge items that are transmissible. For example, design codes or operation manuals provide knowledge that is explicitly coded and structured. Explicit knowledge is recorded in a tangible format and people are able to find it and use it. Generally speaking, explicit knowledge is a piece of documentation such as an article, a report, a webpage, a computer code, a digital image, etc. Tacit knowledge resides in human minds and is hard to articulate using formal methods. It consists of personal experience and involves intangible factors such as personal belief, values, know-hows, etc.
Similar to the concept of learning organization, knowledge management also carries a broad meaning and there are a number of definitions describing a wide diversity of activities. Firestone and McElroy (2003) reviewed various definitions proposed by contemporary knowledge management authors (Malhotra, 1998; Sveiby, 1998; Knapp, 1998; University of Kentucky, 1998; Wiig, 1998; Wenig, 1998; Murray, 1998; Davenport, 1998) and concluded
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that they all have some weaknesses and thus do not cover the full spectrum of knowledge management. They have offered their own definition of knowledge management at a higher level as “a management discipline that seeks to enhance organizational knowledge processing”. McAdam and McCreedy (1999) summarized the common aspects in most definitions as: - IT is a tool to enable the knowledge management but not its essence. - Knowledge management is not just about managing knowledge itself, people and learning issues are equally important. - Knowledge management is the combination of organizational strategy and management processes.
Managing knowledge needs to address all aspects of knowledge. Onions and Orange (2002) view knowledge as having three dimensions: - Known (ontology): what is known - Knower (knowledge systems): the viewpoint and context introduced by the mind of the holder of what is known – the who, the why and the where. - Knowing (epistemology): the processes associated with knowing what is known, specific to the knower, and the how and some of the why. From this point of view, knowledge management is not just about the “knowledge” itself. It also concerns the system that encapsulates the knowledge and the process through which the knowledge is learned.
Knowledge management is an important approach for organizations to achieve the goal of becoming learning organizations. Robinson et al. (2005) mentioned that implementing knowledge management enables an organization to learn from its corporate memory, share knowledge, and identify competencies in order to become a forward-thinking and learning organization. Hansen et al. (1999) summarize two distinct strategies for developing
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knowledge management systems: codification and personalization. A codification strategy tends to be heavily technology focused and is concerned with the management of explicit knowledge captured and leveraged using IT-tools. Knowledge-based expert systems, artificial intelligence and data-mining tools fall under this category. They normally attempt to codify knowledge through the use of ICT tools. Personalization lies at the other end of knowledge management and revolves around tacit knowledge using non-ICT tools or human interactive systems such as knowledge sharing networks (Dyer and Nobeoka, 2000), a community of practice (Wenger et al., 2000), brainstorming, and post-project reviews, etc. This is exactly the kind of knowledge management needed to capture the human dimension. For example, within communities of practice, newcomers learn from old-timers by being allowed to participate in certain discussions or tasks relating to the practice of the community (Hildreth et al., 2000).
Appendix A: Knowledge Management and Learning Organization
11 APPENDIX B HUMAN COMMUNICATION
Communication is the process of transferring information from a sender to a receiver with the help of a medium. Grant and Mergen (1996) break communication down into technical and social communication. Technical communication means transferring information through various technologies or mechanisms. Current organization-science literature often equates information and explicit knowledge (Kogut and Zander, 1992). This is to claim that technical communication is the transfer of explicit knowledge. Social communication focuses on people. It emphasizes the transfer of tacit knowledge that is intuitive, contextual, and linked to experience, and past memories. Tacit knowledge is difficult to codify and document, and as such, it is hard to transfer. Social communication takes place between people through social connections, for example community of practice.
B.1 Traditional Linear Communication Model Many earlier studies of communication acknowledge linear communication models (Rogers and Kincaid, 1981). Most linear communication models are the adaptations of Shannon and Weaver’s (1949) model of communication, in which a message is sent by an information source via a communication channel to a destination (Figure B-1). This kind of model is linear because communication is modelled as the one-way flow of a message from a source to a destination. Berlo (1960) presents a similar model and summarizes six elements of a communication process: a communication source, an encoder, a message, a channel, a decoder, and a communication receiver.
Several points of criticism have been raised against the linear communication model as insight into modes of communication has increased. The first criticism of the linear model is
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its failure to account for the two-sided nature of communication (Rogers and Kincaid, 1981). Meijers (2002) also argues that communication focuses on shared understanding. Transferring a message is only a part of communication; the other part should be the response of the message receiver based on his/her understanding of the message. Another criticism is that linear communication models underexpose the meaning and interpretation of a message. In order to establish shared understanding between message source and message destination, both parties involved must share common knowledge about the message.
Figure B-1: General Communication System (Shannon and Weaver, 1949)
B.2 Message Delivery Mechanism Franklin and Zdonik (1997) mention three characteristics to describe communication styles: the initiator of information transfer (client pull vs. server push), the frequency (aperiodic vs. periodic), and the number of information receivers (Unicast vs. 1-to-N). - Client Pull vs. Server Push: Pull-based communication means that the transfer of information (the communication) from the source to the destination is initiated by the client (the information receiver). By contrast, push-based message delivery means that the information source initiates the communication without any request from the client. - Aperiodic vs. Periodic: Periodic communication is based on a pre-arranged schedule, and aperiodic communication is event driven.
Appendix B: Human Communication 242
- Unicast vs. 1-to-N: Based on the number of receivers, a communication can be identified as unicast style or 1-to-N style. With unicast communication, a message is sent from an information source to only one receiver, while 1-to-N communication allows multiple receivers to get the same message sent by a single information source.
Combining the above-mentioned three characteristics, there are several message-delivery mechanisms as shown in Figure B-2, which have been adapted from (Franklin and Zdonik, 1997). No one single delivery mechanism can fit all communication needs; each style has it own application ambience.
Unicast Request-Response Aperiodic 1-to-N Request-Response w/ snooping Pull Unicast Polling Periodic 1-to-N Polling w/ snooping
Unicast Triggers
Communication Communication Aperiodic 1-to-N Publish/Subscribe Push Unicast Reminders Periodic 1-to-N Broadcast Disks
Figure B-2: Message Delivery Mechanisms, adapted from (Franklin and Zdonik, 1997)
B.3 Communication Channels Broadly speaking, there are two kinds of communication channels: face-to-face communications and distant communications. Face-to-face communications require parties to physically meet each other. Distant communications normally need the help of certain ICT to
Appendix B: Human Communication 243
connect parties at a distance. In-person communications include meetings, workshops, conferences, panel discussions, and so on. Distant communications include mailing/shipping, faxing, telephone calling, physical presenting (signs or signals) and many computerized channels such as e-mailing, audio/video conferencing, instant messaging, web presenting, automated transferring between computer systems, etc. There is no one single preferred communication style, nor is one specific channel used for all communication needs. In many circumstances, especially for important or complicated messages, the combination of several channels is more appropriate for better reliability.
Cheng et al. (2001) indicate four major criteria by which to choose from available channels for a certain communication need: amount of information required, instant information required, effective communication required, and efficient communication required. There are also some other considerations in selecting communication channels such as security requirements (in-person meetings are more secure than many other channels), the legal requirements (postal mail might be required for some legal reasons), and cost (e-mail could be much cheaper than long distance calls for international communications).
Appendix B: Human Communication
12 APPENDIX C WEB-BASED PROJECT MANAGEMENT SYSTEMS
A PCN is a comprehensive construction project management system. The project team through this system share project specific documents, communicate each other, and manage the workflow. A PCN also serves as a repository for documents or as an online document management system for a project team. The common services include displaying the most updated project status during different project life cycle phases for participants’ review and markup, backing up files daily, keeping a document revision history and automatically highlighting unread information/revision, tracking the logon/action history, and providing online meeting function to allow virtual conference. Various members of the construction team can upload or download drawings and other construction documents through a PCN. A PCN is normally hosted on a server provided by an Application Service Provider (ASP). An ASP does not require users to install software in house on their local server. Instead, an ASP hosts all project information off site, allowing users to access details through any Web browser from anywhere at anytime. The monthly cost for using a PCN usually depends on the intensity of the usage of the database. Table C-1 shows major PCN products.
A PIP is an information/document library, providing general information for the participants in a construction project. A project team can find most information that a project team might use throughout the life cycle of a construction project by logging on a PIP server. This information may include codes and permits, economic trends, product information, cost data, and project planning information. Most PIPs offer free services to members since their major income is composed of advertising fee coming from product manufacturers/distributors, while others charge a monthly subscription fee. Table C-2 shows major PIP products.
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A PPE serves mainly the procurement of construction materials and services to streamline the procurement cycle. The system provides electronic bidding and procurement services, which generally allow users to view online catalogs of updated products and services, transmit Request For Quotes (RFQs), exchange cost-related data, review work packages, and conduct bidding and procurement online. Table C-3 shows major PPE products.
Table C-1: Major PCN Products PCN Product Company/ASP URL ActiveProjectTM Framework Technologies www.activeproject.com BuzzsawTM Autodesk www.buzzsaw.com BuildOnlineTM BuildOnline.com www.buildonline.com CAM ConsoleTM LoadSpring Solutions www.loadspring.com Citadon CWTM Citadon www.citadon.com Constructw@reTM Constructware.com www.constructware.com e-BuilderTM MP Interactive www.e-builder.net Edificium Edificium.com www.edificium.com MH2.com MH2 Technologies www.mh2.com OnlineBuildingsTM OnlineBuildings.com www.onlinebuilding.com ParagonTM Vianovus.com www.vianovus.com ProjectEDGETM Edgewater Services www.projectedge.com ProjectGrid.com ProjectGrid.com www.projectgrid.com ProjectmatesTM Systemates www.projectmates.com ProjectSolve® Company 39 www.projectsolve.com ProjectTalkk Systems Meridian Project www.projecttalk.com ProjectVillageTM ProjectVillage.com www.projectvillage.com Tririga IBSTM Tririga www.tririga.com VieconTM Bentley Systems www.viecon.com Vista 2000TM Market Street Technologies www.marketstreet.com Web4EngineersTM Web4 www.web4engineers.com 4ProjectsTM 4Projects www.4projects.com
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Table C-2: Major PIP Products PIP Product Company/ASP URL Akropolis.net Akropolis www.akropolis.net 4specs 4specs.com www.4specs.com Biw.co.uk BIW Technologies www.biw.co.uk BuildersPlanet.com BuildersPlanet.com www.buildersplanet.com Building.com Building.com www.building.com Buildingonline.com Buildingonline.com www.buildingonline.com CMD First SourceTM CMD First Source.com www.cmdfirstsource.com DesignArchitecture DesignArchitecture.com www.designarchitecture.com e-idc.com e-idc.com www.e-idc.com Handyman Online Handyman Online.com www.handymanonline.com HomePro.com HomePro.com www.homepro.com ImproveNetTM ImproveNet.com www.improvenet.com NationalContractors.com NationalContractors.com www.nationalcontractors.com StartMyHome.com StartMyHome.com www.startmyhome.com
Table C-3: Major PPE Products PPE Product Company/ASP URL BuildPoint.com BuildPoint.com www.buildpoint.com BuildscapeTM Buildscape.com www.buildscape.com BidA/E/C.com BidA/E/C.com www.bidaec.com BidExpress.com Contractors Online www.bidexpress.com BidHostTM eBid Systems www.ebidsystems.com Contractors eSourceTM Contractors eSource www.contractorsesource.com Cprojects.com Cprojects.com www.cprojects.com eu-supply.com eu-supply.com www.eu-supply.com PurchaseProTM PurchasePro.com www.purchasepro.com TradePowerTM TradePower www.trade-power.com
Appendix C: Web-based Project Management Systems
13 APPENDIX D WEB 2.0 AND ITS PRINCIPLES
D.1 What is Web 2.0? The Web has been significantly reforming people’s lifestyles since a working Web first publicly available on the Internet in 1991. The Web is a system of interlinked hypertext documents which are called Web pages containing texts, and/or multimedia (images, sounds, videos). The Web is generally used to present human-understandable materials such as texts (in natural languages), images, sounds, videos, etc. This Web is named Web 1.0, although the argument for naming different generations of the Web has never stopped. This system is built upon the Internet which works on TCP/IP protocols (Figure D-1).
The first extension of the Web beyond presenting static web contents is Web applications. A Web application is an application that is accessed via the Web over a network. The popularity of Web applications is due to the ubiquity of the client end. There is no need to install or update any client-end software and the applications are normally compatible with most popular browsers. A Web application usually has a three-tiered structure (Figure D-2). The early Web applications strictly followed a server/client model, meaning that one user only communicates with the server – there is no interaction between different users and the content of the database is provided by the server side.
Web Server Web Presentation
Web Browser (HTML)
Internet (TCP/IP)
Figure D-1: Web 1.0 Structure
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DatabaseDatabase Web Apps Dynamic Web Content Technology (ASP/JSP) Web Browser (HTML) Web Server Web Presentation
Web Browser (HTML)
Internet (TCP/IP) Figure D-2: Web Application Structure
Web 2.0 is the extension of Web 1.0 on Web services regarding online collaboration and interaction between Web users. At the beginning, Web services mainly focused on one-to-many services, i.e., many application users (service consumers) could access a service site and interactively communicate with the Web server (service provider). This service mode is still popular in many industries, for example, in online banking and e-government services. However, in many other circumstances, people using the Web are not satisfied with being passively fed by the Web server, and they have something valuable to say and want to say it. In order to fulfill this requirement, the Web has been shifted from a tool for online presenting and service to a platform that enables user participation. People, as the participants of new online services, can create, update, organize, share, remix, and critique the content. The comparison of Web 1.0 services and Web 2.0 services is like a lecture versus a conversation. A lecture style means that a small number of professors inform a large audience of students, while a conversation style means that all professors and students are mixed and broken up into discussion groups where everyone (a professor or a student) has an opportunity to speak and share views. Figure D-3 shows how Web 2.0 services are structured on top of traditional Web 1.0 applications. A user-contributed content is the key difference between Web 2.0 applications and Web 1.0 applications.
Web 2.0 describes the new trend of using the Web to enhance creativity, information sharing, and collaboration among users. The term became notable after the first Web 2.0 conference
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hosted by O’Reilly Media in 2004 (Graham, 2005). Although the term suggests a new version of the Web, it does not refer to any new technology or technical specification. It only refers to the new way people design and use Web services. The fundamental difference is the social phenomenon of Web 2.0 – the shift away from a powerful few to an empowered many.
User Contributed Content + Database Web 2.0 Apps ASP/JSP+sharing (wikis, blogs, folksonomy) (Social Web) Web Browser (HTML) Database Dynamic Web Content Technology (ASP/JSP) Web Apps Web Browser (HTML) Web Server Web Presentation Web Browser (HTML)
Internet (TCP/IP)
Figure D-3: Web 2.0 (Social Web) Structure
Web 2.0 is also called the Social Web in many circumstances, because it emphasizes the features of social interaction among users (see Section 2.4). Some Web 2.0 applications (e.g., Facebook, Frickr and del.icio.us) make user interaction a top priority to achieve the unique services they provide, while others, such as Amazon and Google, take advantage of user contribution to provide value-added services, i.e., user interaction may not be perceived by people using the service but is incorporated at the server end. All Social Web services emphasize the linking of people. In traditional networking terms, what are being linked are devices or objects – telecommunication networks link telephones via phone numbers, the Internet links computers via IP addresses, and the traditional Web links documents or Web pages via URLs. Now, for the first time, human beings are linked together by the Social Web.
D.2 Web 2.0 Giants Amazon was one of the first major companies to sell goods by Internet. Jeff Bezos, Amazon’s founder and CEO, has been a pioneer in the practice of Web 2.0. Amazon allows for and uses
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a large amount of user-generated content. First, the website offers an opportunity to all users to write reviews (now even upload video reviews) for the items they are selling. A potential buyer can benefit from these reviews and make a better decision. Second, it allows you to share the item you are viewing with your friends and discover all online sellers of this item. Third, the shopping behaviour of customers is observed, and the overlapping items in the baskets of several users are used as a measure of similarity. Associated items that are not yet overlapping are then recommended to shoppers. eBay as an online auction and shopping website is full of user-generated content. eBay itself does not sell anything, but eBay’s product is the collective activity of all its users (O’Reilly, 2005). As an enabler of a platform, eBay enables its users to list items for sale or auction, to buy or bid for an item, to rate and review the sellers and buyers, and to save their favourite eBayers and even form a private community.
Google has been implementing Web 2.0 service since the first day it entered the business. Google Search as its flagship product was able to be the undisputed search-market leader largely because of PageRank. PageRank is a method of using the link structure of the Web, rather than just the characteristics of documents to provide better search results (O’Reilly, 2005). Basically, a Web page’s rank depends on the number of pages linking to this page and the ranks of those pages. The input of others (the evaluations made on other Web pages) helps Google to evaluate a Web page.
D.3 Web 2.0 Principles Tim O’Reilly (2005) has summarized seven principles for Web 2.0 applications. Table D-1 shows the major Web 2.0 services and their embedded principles. - The Web as a platform. The Web is no longer a tool or a service but a platform for interaction and communication.
Appendix D: Web 2.0 and Its Principles 251
Table D-1: Comparison of Major Web 2.0 Services
Web as platform Harnessing Collective Intelligence the Date is Intel Inside End of Software Cycle Release Lightweight Programming Models Software above of the Level Device Single Rich User Experiences
Amazon √ √ √ √ √ √ √
eBay √ √ √ √
Google √ √ √ √ √ √ √
Wikipedia √ √ √ √
Flickr √ √ √ √ √ √
del.icio.us √ √ √ √ √
MySapce √ √
Facebook √ √
YouTube √ √ √ √ √
LinkedIn √ √
Second Life √ √
Craigslist √ √ √
Kijiji √ √ √
Digg √ √ √ √
- Lightweight programming models. More users will be attracted and reached by using lightweight programming models. - Harnessing collective intelligence. This principle is the key to ensuring the success of those giants born in the Web 1.0 era such as Amazon, eBay, and Google, who have survived to lead the Web 2.0 era. They all treat user-generated content as their greatest asset and provide a way to help each user benefit from the contribution of everyone else. - Data is the next Intel Inside. Most successful Web 2.0 companies have specialized
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databases: Google’s Web crawl, Yahoo!’s directory, Amazon’s ASIN, and eBay’s database of its products and sellers. - End of the software release cycle. In the Web 2.0 era, software needs to be delivered as a service rather than a product. - Software above the level of a single device. In order to achieve collective intelligence, the system must integrate enough participants. - Rich user experiences. The principle of rich user experiences has been accepted by many Web 2.0 companies and AJAX is now popular in many online services.
Appendix D: Web 2.0 and Its Principles
14 APPENDIX E SEMANTIC WEB TECHNOLOGY AND ONTOLOGICAL ENGINEERING
E.1 Semantic Web Technology The Semantic Web is an evolving extension of the HTML Web in which web content can be expressed not only in natural language, but also in a form that can be understood, interpreted and used by software agents, thus permitting them to find, share and integrate information more easily (Cardoso, 2007). Aiming at the objective of processing information published on the Web by computers, the Semantic Web has been proposed as a solution for interoperability and integration between systems and applications. It derives from W3C director Tim Berners-Lee's vision of the Web as a universal medium for data, information, and knowledge exchange.
The strategy of semantic technology to achieve interoperability is its way to process data. A piece of data (either structured like a database or semi-structured like a webpage or unstructured like a text) is described by tags from a metadata system. These metadata tags are organized into logical structures called ontologies, which capture the logical and conceptual relationships between tags. The ontology-based description of data is the semantic structure of data content. The interoperability of several data sources is realized by aligning and mapping the ontologies describing all data sources. Semantic technologies offer an opportunity for several innovative applications by achieving interoperability over the amassed data, such as information integration, intelligent search, semantic web services, or proactive and autonomic computing.
The Semantic Web is the specific application of semantic technologies to the Web for explicitly defining the meaning of Web information and effectively harnessing the
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co-operation of various online applications. Berners-Lee et al. (2001) indicate that the Semantic Web is not a new Web but an extension of the existing Web in which information is given well-defined meaning, better enabling the co-operation of computers and people. The Semantic Web does not replace the existing Web, but builds on it and enables better interoperability and further capabilities. In contrast to the existing Web, which focuses on identifying (with URIs), displaying, and publishing Web information, the Semantic Web focuses on the sharing and processing of online information (Rittgen, 2007).
The term Semantic Web is also referred to as Web 3.0 by many people, although the pros and cons of this kind of renaming of different versions have been debated for many years. Objectors argue that many of the technology components of “Web 2.0” have existed since the beginnings of the internet and that Web 3.0 is only another extension of the traditional Web but not necessarily to be built on Web 2.0. In fact, both the Social Web and the Semantic Web are extensions of traditional web service. If Web 1.0 can be seen as a Web-as-information-source, then Web 2.0 is the Web-as-participation-platform and Web 3.0 is the Web-as-interoperable-data source. Figure E-1 shows the extensions of HTML Web on different focuses to enable Social Web services and Semantic Web services.
Web 3.0 Apps Knowledgebase User Contribution Web 2.0 Apps ASP/JSP+Semantics ASP/JSP+sharing (Semantic Web) (Social Web) Semantic Web Browser Web Browser Database Dynamic Web Content Technology (ASP/JSP) Web Apps Web Browser (HTML) Web Server Web Presentation Web Browser (HTML)
Internet (TCP/IP)
Figure E-1: Semantic Web Structure
The Semantic Web is made up of several layers as shown in Figure E-2. The lower layers
Appendix E: Semantic Web Technology and Ontological Engineering 255
(URI, Unicode, and XML) are already well-established standards used in the existing Web, while the higher layers are specific to the Semantic Web – they are currently either operational and partially in implementation or are still under development. Full details about each element and its current status are available at the W3C’s Web Site (w3.org).
Figure E-2: Semantic Web Layers (Berners-Lee and Swick, 2006)
Similar to the Social Web, many of the key issues related to the Semantic Web are social, rather than technical. Rittgen (2007) summarized several issues and challenges: - Semantic technologies rely on the semantic markup of data, but the amount of data that has been semantically marked up (either by data owner or data users) is still very trivial in contrast with the volume of available digital data. Moreover, there is no known process for automatically performing data markup. - There is a need to develop appropriate tools and techniques to support large-scale applications dealing with large volumes of data and querying. - The core component of the Semantic Web is ontologies which are supposed to be developed by domain experts. Along with the advancement of ontology-building tools, some non-experts are able to create their own ontologies without formal logical or
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XML-based notations. - Different semantic systems may have different representations on the same data. Finding a way to quickly and correctly map the meanings in these systems remains a challenge in semantic technologies. - Real-world data tends to be imperfect for purposes of deductive reasoning. Semantic Web technologies need further optimizations to deal with incomplete, uncertain, and problematical data. - In order to take full advantage of the proactive and autonomic services of intelligent agents, the access rights (who can access what data at what level) have to be fully and carefully addressed for reasons of privacy, security, and digital-rights protection.
The cornerstone of semantic technologies is the markup language used to explicitly annotate the data to be processed. This markup language which encapsulates the common understanding of a certain domain among a set of domain participants is called ontologies. The next section will discuss topics in ontological engineering in greater detail.
E.2 Ontological Engineering E.2.1 Ontology Development Methodologies and Methods A number of methodologies and methods for developing ontologies have been reported. There is no single correct methodology to fit all development projects, and there is no one methodology is superior to another. The selection of one or another totally depends on the characteristics of the ontology to be developed. Also, the process of ontology development is usually iterative (Noy and McGuinness, 2002). Gómez-Pérez et al. (2004) have summarized several methodologies with significant influence in ontology development: - The Cyc methodology (Lenat and Guha, 1990). This is the methodology employed to develop the Cyc knowledge base. This methodology focuses on the support of knowledge acquisition activity. The objective is to capture implicit common-sense knowledge and
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make it explicit in the ontology - The Uschold and King’s (1995) methodology. This methodology covers more aspects of the ontology development lifecycle. It proposes four phases: (1) identifying teh scope, purpose, and intended use of the ontology, (2) capturing the domain knowledge and coding the ontology with the integration of existing ontologies, (3) evaluating the ontology with respect to its pre-defined objectives, and (4) documenting the ontology to include all concepts, definitions, relationships, and axioms. - The Grüninger and Fox’s (1995) methodology. This methodology is similar to Uschold and King’s work, but emphasizes the use of competency questions to formally identify the possible application scenarios and to evaluate the completeness of the ontology at the end of the development phase. - The Sensus methodology (Swartout et al., 1997). This is a top-down approach where the authors propose to identify a set of “seed” terms that are reusable in other ontologies to promote the sharability of knowledge. - Mothontology (Fernández-López et al., 1999). This is a methodology aimed at creating domain ontologies that are independent of their applications. The ontology development processes presented earlier are derived from this methodology. - The on-to-knowledge methodology (Staab et al., 2001). This methodology is based on the analysis of usage scenarios. It has four steps: (1) a kick-off step to capture and specify the requirements, (2) a refinement step to produce a mature and application-oriented ontology, (3) an evaluation step to examine whether pre-defined requirements have all been met, and (4) a maintenance step to maintain the ontology during usage. - The Noy and McGuinness’s (2002) methodology. Noy and McGuinness present a seven-step methodology which includes: (1) determining the domain and scope of the ontology using competency questions, (2) considering the reuse of existing ontologies, (3) enumerating important terms in the ontology, (4) defining the classes and the class hierarchy (the taxonomy) (5) defining the properties of the classes, (6) defining the value
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type, allowed values, cardinality, and other features of the properties of classes, and (7) creating instances.
E.2.2 Ontology Languages The evolution of ontology languages moves in two directions: Artificial Intelligence (AI) based ontology languages and Web-based ontology languages. The 1990s were dominated by AI-based ontology languages, beginning with the CycL (Lenat and Guha, 1990) which was the language used to create Cyc ontology. Later, in 1992, the KIF (Genesereth and Fikes, 1992) was created. KIF stands for Knowledge Interchange Format and was based on first order logic. Ontolingua (Farquhar et al., 1997) was built on top of KIF and became a standard de facto in the ontology community in the 1990s. Other AI-based ontology languages are OCML (Motta, 1999), Flogic (Kifer et al., 1995), and OKBC (Open Knowledge Base Connectivity) protocol (Chaudri et al., 1998).
The development of Web-based ontology languages has received more and more attention because of the increasing growth of Internet applications. These kinds of ontology languages aim at the markup of Web information for explicitly expressing meaning. The syntax of ontology-markup languages is based on existing HTML (Raggett et al., 1999) and XML (Bray et al., 2000), whose original purpose was not ontology development but data presentation and data exchange respectively (Gómez-Pérez et al., 2007). The most important examples of ontology-markup languages are RDF (Lassila and Swick, 1999), RDF Schema (Brickley and Guha, 2004), OIL (Horrocks et al., 2000), DAML+OIL (Horrocks and van Harmelen, 2001), and OWL (Dean and Schreiber, 2004). Those languages were evolved in a sequence from XML through RDF, RDFS, OIL, DAML+OIL, and then OWL, which is currently very popular for Web information markup. Recently, a new ontology language, WSML (de Brujin, 2006) is being developed specifically for Semantic Web Services.
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E.2.3 Ontology Development Tools and Environments Similar to software development, building an ontology is a complex and time-consuming practice, and thus requires an appropriate development environment and tools that will support the work. The earliest tools supporting ontology development activities appeared in the mid-1990s. Following that, more tools and development environments (tool suites) became available in the ontology community. Gómez-Pérez et al. (2007) categorize them into two groups: tools supporting only one specific ontology language and tools supporting multiple ontology languages. The four major ontology development suites are Protégé (Noy et al., 2000), WebODE (Arpírez et al., 2003), OntoEdit (Sure et al., 2002), and KAONI (Maedche et al., 2003).
E.2.4 Ontology Evaluation An ontology needs to be evaluated before any kind of usage (reuse on other ontology development projects or implementation in certain applications). Based on (Gómez-Pérez, 1996), ontology evaluation is a technical judgement of the content of the ontology with respect to a frame of reference. According to Gómez-Pérez et al. (2004), ontology evaluation includes ontology verification, ontology validation, and ontology assessment: - Ontology verification. This ensures that the ontology is built correctly, i.e., that all definitions (written in natural language) are implemented correctly in the ontology. - Ontology validation. This ensures that the ontology’s definitions model the world (for which the ontology was created) correctly. - Ontology assessment. This evaluates the users’ opinion of the content of ontologies, and is the user’s judgement/point of view. Different types of users and applications require different means of assessing an ontology.
In general, the goal of the evaluation process is to determine what the ontology does or does not define correctly. In order to evaluate the content of a given ontology, evaluation criteria
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must first be identified. Gómez-Pérez (1996) has summarized three criteria (consistency, completeness, and conciseness) applied to the evaluation of ontologies. - Consistency. The content of an ontology is consistent if there is no contradictory conclusion to be inferred from valid input definitions. - Completeness. There is no way to prove the completeness of an ontology, but there is a way to prove its incompleteness if at least one definition is missing. - Conciseness. An ontology is concise if: (1) it does not store any unnecessary or useless definitions, (2) it does not contain explicit redundancies between definitions of terms and (3) redundancies cannot be inferred from other definitions and axioms.
E.3 Semantic Web Enabled Knowledge Management in the AEC Industry Researchers began to study the use of Semantic Web technologies and ontologies in the field of knowledge management immediately after the concept of Semantic Web was first introduced by (Berners-Lee et al. 2001.) The vision of the Semantic Web is to offer more intelligent services by facilitating machine understanding of content. Ontologies are an important building block in the applications of Semantic Web because ontologies provide a shared and common understanding of a domain that can be communicated across people and applications. This section will review one research that aims at building an ontology-based tool environment for knowledge management (Sure et al., 2003) and another research that focuses on the collaborative information management in construction projects (Anumba et al., 2008.)
The EU IST-1999-10132 project On-To-Knowledge (Sure et al., 2003) develops methods and tools to employ the full power of the ontological approach for facilitating knowledge management. The On-To-Knowledge tools will help knowledge workers to access company-wide information repositories in an efficient, natural and intuitive way. A key outcome of the On-To-Knowledge project is the resulting software toolset. Several
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consortium partners are participating in the effort to realize in software the underpinning ideas and theoretical foundations of the project. A major objective of the project is to create intelligent software to support users in both accessing information and in the maintenance, conversion, and acquisition of information sources. The tools are integrated in a three-layered architecture. The layers consist of (i) the user front end layer on top, (ii) a middleware layer in the middle and (iii) an extraction layer at the bottom. Each tool represents certain functionalities. The layering allows for a modular design of applications that bundle some or all of the functionalities provided. Most of the tools presented in the figure are described subsequently below. As a minimum requirement all tools support OIL core that has been designed to be exactly the part of OIL that coincides with RDF(S).
The user front end layer has four components: RDFferret, OntoShare, Spectacle, and OntoEdit. RDFferret combines full text searching with RDF querying. OntoShare enables the storage of best practice information according to an ontology and the automatic dissemination of new best practice information to relevant co-workers. Spectacle is a content presentation platform featuring custom-made information presentations, aimed at supporting the information needs of its users. OntoEdit is a collaborative ontology engineering environment that is easily expandable through a flexible plug-in framework.
The Ontology Middleware Module at the middle layer can be seen as administrative software infrastructure that makes the knowledge management tools easier for integration in real-world applications. The major features supported are (i) change management for ontologies allows work with, revert to, extraction, and branching of different states and versions, (ii) access control (security) system with support for role hierarchies including comprehensive and precise restrictions (down to object/record-level) that enable business-logic enforcement, and (iii) meta-information for ontologies, specific resources (classes, instances), and statements.
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The extraction layer consists of two parts: OntoExtract and OntoWrapper. OntoExtract semi-automatically extracts information from texts of natural language and raw information sources were structured on the basis of an ontology by OntoWrapper.
There is a strategic need to effectively manage the sheer size of information and knowledge on the Web and company intranets. On-To-Knowledge takes a necessary step in this process by providing innovative tools for semantic information processing and thus for much more selective, faster, and meaningful user access.
Anumba et al. (2008) has explored the use of Semantic Web technologies to meet the challenges of collaborative project information management. Construction projects are characterised by the strong need for close collaboration between AEC team members, and require an effective approach to the management of information from diverse sources. Current IT tools enable designers and contractors to operate in an electronic era where large volumes of information are wrapped into digital documents and distributed through the internet. However, many project deliverables are not readily deliverable, in the information sense, because the information senders and receivers cannot adequately understand one other, or the sender can neither identify all receivers nor distribute information to them in a timely fashion. Meanwhile, the information from the sender cannot be directly used in the receiver’s database due to data structure or language incompatibility issues. These communication and interpretation tasks are often processed manually by humans leading to errors and low levels of efficiency.
These inadequacies of collaborative project information management can be effectively tackled within the Semantic Web environment. Semantic Web technologies support communication between project team members at the information level rather than at the
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document level. They enable the meaning of electronic information to be identified by computers and interpreted between information repositories. Using the framework proposed by Anumba et al. (2008), design and construction information, from heterogeneous repositories connected to the web, are given well-defined meaning, in computer-processable languages (XML, RDF and OWL). This enables several interactive activities between people and computers, such as interpreting the content of drawings, customized responses to queries, and update alerting. The semantics of project information is used in the collaboration between team members to avoid information misunderstanding, loss and overload during the information sharing and transfer process, as shown in the terminology sharing example, as well as the data integration and data/process dynamics in the construction example.
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15 APPENDIX F DESCRIPTION OF AR-ONTO CONTENT
F.1 Individual Actor Professional Individual – Natural and Applied Science The NOC has six skill types in this category and the AR-Onto fully covers two types: (1) professionals in architectural, urban planning and land surveying sectors, and (2) engineering professionals. This ontology also partially covers professionals in computer and information system domain as supporting concepts of the construction industry. The major actors modeled in this class include: Architect, Landscape Architect, Land Surveyor, Urban Planner, Land Use Planner, Electrical Engineer, Electronics Engineer, Mechanical Engineer, and Civil Engineer. This is one of the most important categories in modelling construction actors. This class covers about 110 subclasses of professional actors including 35 subclasses of professional civil engineers.
Professional Individual – Others Other than professional actors in natural and applied science sector, the AR-Onto covers some other important professor actors to support the construction industry. Some important concepts include Lawyer, Notary, Accountant, etc. Many concepts listed as professional individuals in business, finance, and administration sectors are classified as roles.
Skilled Individual – Natural and Applied Science The NOC has occupations in all nine skill types in Level 2 and Level 3, and similar to Professional Individual, the AR-Onto focuses on the actors in natural and applied science sector, which covers Architectural Technician, Drafting Technician (including draft person in a variety of fields), Land Survey Technician, Mapping Technician, Computer and Information
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Systems Technician, Civil Engineering Technician (including Building Specialist, Concrete Technician, Construction Specialist, Environmental Technician, Material Testing Technician, Railway Technician, etc.), Cost Estimator, Electrical Technician, Mechanical Technician, etc.
Skilled Individual – Trade, Transport, and Equipment Operation This subclass covers the actor concepts found in a variety of trades, transport and equipment operation. They include: Carpenter, Electrician, Heavy Equipment Operator, Vehicle Driver, Bricklayer, Concreter, Drywall Worker, Plasterer, Tilesetter, Metal Worker, Flooring Worker, Glass Worker, Insulator, Painter, Roofer, etc.
Skilled Individual – Others The AR-Onto covers some other important skilled actors in other skill types to support the construction industry. Those actor concepts are technicians or operators in construction material manufacturing, clerical and technical individuals in human resources and business services, or supporting individuals in financial or legal services.
Management Individual From the philosophical foundation of differentiating actors and roles, most management occupations can be seen as roles, because those positions are essentially the collection of responsibilities, rights, and obligations which are all dynamic attributes of an entity based on different contexts. On the other hand, this does not mean there is no management actor in the taxonomy. It was said that a role need to be situated in a context (for example a process or a project), but the ontology should accommodate some individuals who have management as their profession but not currently engage in any contexts. This situation also applies to individuals who have interests on management occupation but not “formally” an actor to this industry yet. One example is a university student majoring in construction management. People in this group can simply identify themselves and define their profiles as general
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management individuals in proposed ontology.
General Labourer General labourers in the construction industry are mainly the helpers and other general worker in construction trades such as Asphalt Mixer, Asphalt Spreader, Cement Mixer, Concrete Breaker, Drywall Sander, Construction Cleaner, etc.
F.2 Organizational Actor Organization in Construction Industry Major organizations in this class include: General Contracting Company, Design Build Contracting Company, Construction Management Company, Construction Project Development Company, and Specialty Trade Contracting Company which covers equipment, finishing, foundation, structure, and exterior contracting companies.
Organization in Professional Scientific Technical Services Industry Major organizations in this class include: Architectural Services Company, Engineering Services Company, which includes civil, environmental, electrical, mechanical and many other engineering services, Building Inspection Company, Drafting Services Company, Landscape Architectural Services Company, Surveying and Mapping Services Company, Testing Services Company, Environmental Consulting Services Company, Management Consulting Services Company, and many specialized service companies such as Graphic Design Services Company. Some services companies in this class play a supporting role to the construction industry, for example, Accountant Office, Bookkeeping or Payroll Services Company, Tax Services Company, and Legal Services Company.
Organization in Manufacturing Industry Major organizations in this class include: Architectural Metals Manufacturing Company,
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Structural Metals Manufacturing Company, Electrical Equipment Appliance and Component Manufacturing Company, Hardware Manufacturing Company, HVAC Equipment Manufacturing Company, Cement Product Manufacturing Company, Concrete Product Manufacturing Company, Clay Product and Refractory Company, Glass and Glass Product Manufacturing Company, Lime and Gypsum Product Manufacturing Company, Wood Product Manufacturing Company, Transportation Equipment Manufacturing Company, etc.
Organization in Other Industries There are some organizations that are not within the construction industry but to support construction projects. Some of them are: organizations in the finance and insurance industry such as Bank or Insurance Company, organizations in the utility industry such as Electrical Power Distribution Company or Water Supply Company, organizations in the transportation industry such as Truck Transportation Company, organizations in non-government services industry such as Business Association or Labour Union, organizations in real estate industry such as Real Estate Brokerage Company, etc.
F.3 Role Role of Process The concept of process is the core in modelling construction knowledge. Many costly delays and reworks are cause by insufficient process engineering and integration. As such, process is the core context in AR-Onto and other parallel ontological modelling efforts conducted by author’s colleagues. A process is part of a project phase. A process could be composed of a set of sub-processes. A sub-process could be composed of a set of activities. An activity could be composed of a set of tasks. The life cycle of processes and their sub classes is composed of a set of stages. The default stages recognized in the AR-Onto are initiation stage, planning stage, implementation stage, monitoring and control stage, and closure stage. Accordingly there are roles for each stage: Process Initiator, Process Planner, Process Executor, Process
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Controller, and Process Terminator. Those stages and corresponding roles are typical to any context with temporal aspects such as Event or Project. From the perspective of functions, roles of process include: Process Administrator, Process Auditor, Process Coordinator, Process Director (similar to Process Manager), Process Evaluator, Process Observer, Process Scheduler, Process Stakeholder, etc.
Role of Event Similar to roles of process, from the perspective of event stages, roles include Event Initiator, Event Planner, Event Executor, Event Controller, and Event Terminator. From the perspective of functions, roles of event include Event Analyst, Event Coordinator (similar to Event Facilitator), Event Organizer (similar to Event Manager), Event Evaluator, Process Stakeholder, etc.
Role of Organization An actor could hold different organizational roles. An actor cold be a Senior Manager (for example, Organization Director, Organization President, Organization Principle, Organization Chairperson, etc.), a specialty manager (for example, Organization Financial Manager, Organization Human Resource Manager, Organization Operation Manager, etc.), or an administrative role (for example Organization Secretary or Organization Auditor). An actor could be holding a managerial role at a department level such as Department Administrator or Department Supervisor. In a specific team, an actor could be a Team Leader (for example the Foreman or Forewoman in a specialty trade) or a Team Player.
Role of Product The AR-Onto recognizes two major categories of product: Physical Product (manufactured products such as a scaffold or constructed products such as a building) and Non-Physical Product which includes Knowledge (for example, the best practice of trenchless construction),
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Knowledge Item (for example, a request for proposal), Service (for example, a consulting service of financial management), and Other Non-Physical Product (for example, a financial product like Insurance). Some roles of products are common to all types of product. They are Product Owner, Product Consumer, Product Inspector, Product Producer, Product User, Product Supplier, Product Designer, Product Analyst, etc. Some roles of products are specific for a certain type of products. For example, some physical products such as buildings or equipment require roles Product Operator and Product Maintainer. For a manufactured product, the role of Product Producer is called Product Manufacturer, while for a knowledge item, the role of Product Producer is called Knowledge Item Creator.
Role of Project Similar to roles of processes and roles of events, roles of projects are identified from the perspective of temporal stages and the perspective of functions. From the temporal perspective, roles include Project Initiator, Project Planner, Project Executor, Project Controller, and Project Terminator. From functional perspective, roles include Project Client, Project Consultant, Project Contractor, Project Auditor, Project Designer, Project Estimator, Project Financer, Project Inspector, Project Scheduler, Project Surety, Project Manager, etc.
F.4 Attribute Important physical attributes for individual actors include: - Demographic attributes. Those attributes include Age, Gender, Ethnicity, Marital Status, Religion, etc. - Contact attributes. Those attributes include: (1) Names: Full Name, First Name (synonym of Given Name), Middle Name, Last Name (synonym of Family Name). Please note, here the concept First Name, Middle Name, and Last Name are not subclasses of Full Name, because they are not a kind of full name, but a part of full name.
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(2) Addresses: Mailing Address and Email Address (3) Phone numbers: Work Phone Number, Home Phone Number and Cell Phone Number (synonym of Mobile Phone Number) (4) Fax Number - Communication attributes: Communication Language - Affiliation attributes: Each individual actor will have affiliation to a variety of contexts in the domain of construction knowledge, including Event Affiliation (in which even the actor holds a role), Geographic Affiliation (location of the actor), Organizational Affiliation (in which organization the actor holds a role), Process Affiliation (in which process the actor holds a role), Product Affiliation (about which product the actor holds a role), and Project Affiliation (on which project the actor holds a role).
Important physical attributes for organizational actors include: - Age. Years of an organization in business. - Contact attributes. Those attributes include Full Name, Mailing Address and Email Address, Work Phone Number, Fax Number. - Communication attributes: Communication Language - Affiliation attributes: Each organizational actor will also have a set of organizational affiliation attributes, including Event Affiliation (in which even the organization holds a role), Geographic Affiliation (location of the organization), Process Affiliation (in which process the organization holds a role), Product Affiliation (about which product the organization holds a role), and Project Affiliation (in which project the organization holds a role).
Important abstract attributes for individual actors include: - Ability. This is an enduring attribute of individual actors that influences performance. Ability attributes include Cognitive Ability (abilities that influence the acquisition and
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application of knowledge in problem solving), Psychomotor Ability (abilities that influence the capacity to manipulate and control objects), Physical Ability (abilities that influence strength, endurance, flexibility, balance and coordination), and Sensory Ability (Abilities that influence visual, auditory and speech perception). There are subclasses for each of those four categories and in total the taxonomy includes 54 ability attributes. - Experience. Experience attributes in this taxonomy mainly refer to related work experience, i.e., know-hows or procedural knowledge gained through the involvement of certain work. Some experience attributes include Technical Work Experience (like Architectural Design Experience or Electrical Wiring Experience), Management Experience, Filed Construction Experience, etc. - Training. This includes On the Job Training, In Plant Training, On Site Training and Apprenticeship. - Skill. A skill is the learned capacity to carry out pre-determined results often with the minimum outlay of time, energy, or both. Skill attributes defined in this taxonomy include Basic Skill (for example Skill of Reading Comprehension, Skill of Writing, Skill of Critical Thinking, Skill of Active Learning, Skill of Active Licensing, etc.), Social Skill (for example Skill of Coordination, Skill of Negotiation, Team Work Skill, Skill of Social Perceptiveness, etc.), System Skill (for example Skill of System Analysis, Skill of System Evaluation, etc.), Technical Skill (for example Skill of Technical Design, Skill of Operation, Skill of Installation, Skill of Troubleshooting, etc.), Resource Management Skill (for example Skill of Managing Time, Skill of Managing Material Resource, etc.), Complex Problem Solving Skill and Other Skill. In total this taxonomy includes 35 skill attributes. - Knowledge. The only agreement on defining the concept “knowledge” is there is no single agreed defined presently. Knowledge in the AR-Onto refers to the theoretical or practical understanding of a subject gained through experience or education. This taxonomy includes 46 knowledge attributes which are grouped by skill types similar to
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the NOC 2006: Knowledge in Arts and Humanities, Knowledge in Business and Management, Knowledge in Communications, Knowledge in Education and Training, Knowledge in Engineering and Technology, Knowledge in Health Services, Knowledge in Law and Public Safety, Knowledge in Manufacturing and Production, Knowledge in Mathematics and Science, Knowledge in Transportation, and Other Knowledge. - Information and Knowledge Need. As the objective of this ontology is for applications to facilitate information exchange and knowledge sharing, it is necessary to be able to model the needs of each actor or role for information and knowledge. - Other abstract attributes. Some other abstract attributes for individual actors include Certificate and License, Education Background, Reputation, etc.
Important abstract attributes for organizational actors include are similar to those of individual actors. They include Experience, Information and Knowledge Need, and Reputation.
F.5 Relationship Hyponymy Relationships “is-a” relationships are embedded in concept taxonomies in the AR-Onto.
Meronymy Relationships The AR-Onto includes the following “whole-part” relationships: - “is a part of”. For example, a Specification “is a part of” a Design Package. Similar relationships include “is a component of”, “is a section of”, “is a segment of”, “is a portion of”, etc. - “include”. For example, a Bid Package “includes” a Quotation. Similar relationships include “contain”, “is composed of”, “is consist of”, etc.
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Cross-Tree Relationships The AR-Onto includes the following cross-tree relationships: - Attributive relationships. The most important attributive relationship is “has”. Any given concept in the ontology can be associated with its attributes by this type of relationship. For example, a Project Manager “has information need” about safety, and an Engineer “has skill” of mathematics. Some important attributive relationships for actors and roles are “has knowledge”, “has skill”, “has ability”, “has responsibility”, “has certificate and license”, “has language”, “has information need”, “has experience”, “has education background”, “has name”, “has age”, “has address”, etc. - Functional relationships. The most important functional relationship in the AR-Onto is “play” and “is played by” which link the actors and roles. For example, an Electrical Engineer “plays” a role of Inspector, or a Contractor “is played by” a Plumber. Other functional relationships include “use”, “utilize”, “manifest”, “guide”, “enhance”, “make”, “produce”, “create”, “consume”, etc. - Causal relationships. Causal relationships mainly describe the causes and impacts (or consequences) between concepts. For example, an Owner “influences” a Design. The major causal relationships included in the AR-Onto are “cause”, “result in”, “influence”, “impact”, etc. - Conformance/Constrain relationships. Some frequently used constrain relationships are “is subject to”, “adhere to”, “apply to”, “limit”, “constrain”, etc. For example, a Design Code “applies” to a Technical Design Process. - Action relationships. Some important action relationships include “enact”, “initiate”, “request”, “involve”, “approve”, “accept”, “permit”, “allow”, “reward”, etc. For example, an Owner “rewards” a Contract.
It is not possible to have the AR-Onto exhaustively include all relationships to fully encapsulate domain knowledge about actors and roles in the construction industry. This
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ontology has included most important relationships to model actors and roles, and is easy to extend to cover more relationships.
F.6 Axiom The AR-Onto includes the following cardinality axioms: - An Actor has 1 and only 1 Name, Age, etc. - An Actor has at least 1 Geographic Affiliation, Communication Language, Email Address, Mailing Address, etc. - An Individual Actor has 1 and only 1 Given Name, Family Name, Middle Name, Ethnicity, Martial Status, Educational Background, etc. - An Individual Actor has at least 1 Personal Contact Number, etc. - An Organizational Actor has at least 1 Work Contact Number, etc. - A Role has at least 1 Responsibility, Liability, Right, etc. - An Actor has at least 1 Experience, Knowledge, Skill, Ability, etc. - A Process has at least 1 Process Initiator, Process Controller, Process Executor, etc. - An Event has at least 1 Event Initiator, Event Controller, Event Executor, etc. - An Organization has at least 1 Organization Manager, etc. - A Project has at least 1 Project Initiator, Project Controller, Project Executor, Project Manager, Project Client, etc. - A Product has at least 1 Product Owner, Product User, Product Designer, Project Manager, Project Client, etc. - An Actor plays 1 or more Roles.
The domain level descriptive axioms formulated in the AR-Onto include: - A Government Management Individual “has organizational affiliation” of Government Organization. - A Professional Individual “has educational background” of University or higher.
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- A Management Individual “has knowledge” of Business and Management. - A Role of Event “has event affiliation” of an Event. - A Role of Project “has project affiliation” of a Project. - A Role of Process “has process affiliation” of a Process. - A Role of Product “has product affiliation” of a Product. - A Role of Organization “has organization affiliation” of an Organization. - A Skilled Individual “has skill” of Skill.
The AR-Onto includes the following behavioural axioms: - Relationships “plays” and “is played by” are inverse relationships. If an Actor X “plays” a Role Y, then the Role Y “is played by” the Actor X. - An Actor “involves” in Processes, Events, Projects, etc. - A Role “involves” in Processes, Events, Projects, etc. - An Actor “plays” 1 or more Roles. - A Role of Organization “is played by” an Individual Actor. - An Actor or a Process “produces” a Product.
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16 APPENDIX G DESCRIPTION OF CIKP-ONTO CONTENT
G.1 Individual Actor - Professional Individual. This class gathers all professionals who work in or for the construction industry, including Legal Professional such as Lawyer, Financing Professional such as Accountant, Engineering Professional such as Structural Engineer or Electrical Engineering, Architectural Professional such as Architect, Urban Planning Professional such as Urban Planner, etc. - Skilled Individual. This class covers all technicians (or technologists) in natural and applied science and major trade workers related to the construction industry. Some examples of technicians or technologists include Architectural Technician, various kinds of Draftperson, Land Survey Technician, GIS Technician, a variety of Civil Engineering Technician such as Concrete Technician, Construction Cost Estimator, etc. Some examples of trade workers include a variety of Carpenter, Electrician, Masonry Worker, Metal Worker, Plumber, Equipment Operator, and many others. - General Labourer. This class mainly includes Public Work And Maintenance Labourer such as Garbage Collector and Construction Trade Helper And Labourer such as Asphalt Mixer. - Management Individual. This class includes all individuals who have management work as their profession and do not belong to any technical domains.
G.2 Organizational Actor - Organization In Construction Industry such as different kinds of Contracting Company, Construction Management Company, Construction Project Development Company, etc. - Organization In Finance And Insurance Industry such as Bank who finances
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construction projects, Insurance Company who provides insurances or bonds, Insurance Agency, Insurance Brokerage Company, etc. - Organization In Manufacturing Industry such as Metal Window And Door Manufacturing Company, Cement Manufacturing Company, Glass Manufacturing Company, etc. - Organization In Professional Scientific Technical Services Industry such as Architectural Service Company, Engineering Service Company, Testing Service Company, etc. - Organization In Retail and Wholesale Industry such as Building Material And Supplies Dealer, Electrical Wiring Wholesale Distribution Company, etc. - Organization In Utility Industry such as Electric Power Generation Company, Sewage Treatment Company, etc.
G.3 Product Knowledge Item There are four types of knowledge items in the CIKP-Onto: - Document. This class refers to knowledge items that contain mainly text. The items may include some images but images should not be the major portion of the document. Documents may include Article (for example Academic Paper or Technical Paper), Contract, Letter, Notice (for example Notice To Bidder or Change Order Notice), Package (for example Bid Package), Quotation, Report (for example Budget Report, Specification Report, Environmental Impact Report, etc.), Request (for example Request For Information or Request For Proposal), etc. - Image. This class refers to knowledge items having two-dimensional images as their major content. The most important images as a knowledge item in the CIKP system are drawings such as As Built Drawing, Preliminary Design Drawing, Shop Drawing, etc. Other images may include Map, Chart, Graph, Photo, Painting, etc. - Multimedia Item. This class refers to knowledge items containing multimedia contents such as voice and video. It mainly has two subclasses: Audio Item and Video Item.
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- IT-Related Knowledge Item. This class refers to knowledge items related to information and computer systems, for example Software, Database, Website, and Computer Code.
Technical Knowledge Philosophical debates on the definition of the concept “knowledge” have been carried on for centuries, dating back to Plato’s formulation of knowledge as “justified true belief.” The scope of the CIKP-Onto, however, is only concerned with the technical knowledge that is related to the construction industry and exchanged or shared by industry participants. Some important technical knowledge includes: - Best Practice. Best practice is an idea that asserts that there is a better way to deliver a particular outcome than others. A “way” could refer to many things such as a technique, a method, a process, or an activity. An example of best practice in construction could be Best Practice of Trenchless Technology. - Lessons Learned. “Lessons Learned” document the experience gained during a project, process, or event. Lessons learned are typically negative with respect to identifying process, practice, or systems to avoid in specific situations. Lessons learned can be positive, however, when they identify solutions to problems when they occur. An example of lessons learned in construction could be Lessons Learned Regarding Accident Prevention. - Information. It is recognized in the CIKP-Onto that a piece of information is a kind of knowledge. It may not be as comprehensive as the knowledge people may traditionally feel is necessary to fully solve a problem, but it does contain the elements that can suggest a possible solution to the problem. Therefore, information is a major subject for the CIKP system to handle. The information could be about any actor, action, project, or product. - Other aspects of technical knowledge that are common in the construction industry include Design (for example Architectural Design or Engineering Design), Objective (for
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example Project Objective or Design Objective), Plan (for example Safety Plan or Master Plan), Scope (for example Project Scope), etc.
Physical Product Physical products refer to tangible items that are either manufactured or built. This class includes Basic Physical Product (such as Beam, Column, Footing, etc.) and Complex Physical Product (such as House, Bridge, Highway, Water Distribution System, etc.) There are 522 physical products in the CIKP-Onto: - Basic Physical Product. There are two kinds of basic product: Basic Building Product and Basic Infrastructure Product. The concepts of basic building products have been obtained from previous research conducted by the author (2004) and the concepts of basic infrastructure products have been incorporated into the system from the IPD-Onto (Osman, 2007). - Complex Physical Product. Similar to basic products, there are two kinds of complex products: Complex Building Product and Complex Infrastructure Product. The concepts of complex physical products adopt the concepts outlined in (Zhang, 2004).
Decision Decision is the product of mental processes. A decision is normally a final choice from a set of alternatives. An example of decisions in the construction industry could be Decision of Project Delivery Method which determines the way a project is to be delivered (design-build or design-bid-build). Another common example of a decision is Contract Award, which is the product of a bidding process and determines the contractor of a project.
G.4 Action Events included in the CIKP-Onto are Accident (such as Safety Accident), Administrative Outcome (such as Approval or Rejection), Announcement, Instantaneous Achievement (such
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as End or Start), Natural Event (such as Flood or Tornado), Planned Event (such as Meeting or Workshop), Possible Occurrence (such as Change Occurrence), and Transaction.
There are four major categories of processes (El-Gohary, 2008): - Core Process. Core processes are product-oriented processes that create a primary project product or deliverable such as a design or constructed facility (or parts of it). Core processes are mostly technical and highly dependent on the characteristics and sector of the project, and as such are highly variable from project to project and require technical expertise. There are thus two main types of core processes: Technical Design Process and Technical Construction Process. Technical design processes cover pure technical design processes such as Alignment Design Process, Geometric Design Process, Structural Design Process, etc. Technical construction processes include pure technical field processes such as Concerting Process, Earth Work Process, etc. - Management Process. Management processes enable core processes and ensure that the design and constructed facility (primary project products) are delivered according to project objectives. Management processes are common to most projects. They are related to each other by their performance for a common purpose. The purpose is to initiate, plan, execute, monitor and control, and close a project. They also occur throughout the project life cycle. Management processes include Scope Management Process, Time Management Process, Risk Management Process, etc. - Knowledge Integration Process. Knowledge integration processes use an integrated approach to extensively and formally embed key concepts, knowledge, and experience into a project throughout its life cycle to achieve overall project objectives. An example of knowledge integration processes is Constructability Process which aims at extensively and formally using construction knowledge and experience, as well as key constructability concepts, throughout all project phases to achieve overall project objectives.
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- Support Process. Support processes are necessary to support the other types of processes and include administration processes, communication processes, information management processes, etc. Support processes cannot be ignored and are vital for project success, but they do not serve a direct primary project objective or purpose. These processes are key enablers and indirect influencers for achieving project objectives. They are also highly repetitive (being either periodical or continuous) throughout the project life cycle and within core, management, and knowledge integration processes.
G.5 Value Pattern Some major value patterns included in the CIKP-Onto are: - Gender Value. This class gathers the values of gender for individual actors. The two values are Male and Female. - Geographic Area. This class gathers the values for many location-related attributes such as Product Location for products or Geographic Affiliation for actors. In this class, the concept should be composed with care. For example, Canada is a Geographic Area, and everybody knows Ontario and Quebec are both in Canada, however it is wrong if the taxonomy structure is Geographic Area Æ Canada Æ Ontario (or Quebec). The reason is that Ontario (or Quebec) is a kind of Geographic Area, but it is not a kind of Canada – it is just a part of Canada. As such, in order to have the correct taxonomical relationship, one has to say Geographic Area Æ Canadian Area Æ Ontario or Quebec. If the ontology needs to model more specific areas such as cities or even neighbourhood, the taxonomy structure may be Geographic Area Æ Canadian Area Æ Ontario Area Æ Toronto Area Æ Annex Area. - Language. This class gathers the values for language-related attributes such as Communication Language for actors or Knowledge Item Composition Language for knowledge items. The major values are English, French, Spanish, etc. - Subject Domain. This class gathers the values for domain-related attributes such as
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Knowledge Item Subject Domain or Technical Knowledge Subject Domain. There are hundreds of subject domains and the classification for this research does not need to be very specific, and only needs to cover the most commonly used domains in or related to the construction industry. The higher levels of subject domain include Economic Domain, Political Domain, Social Domain, Legal Domain, Management Domain, and Technical Domain. In the Technical Domain, there are more specialized sub-domains such as Civil Engineering Domain (which may be further divided into Structural Engineering Domain, Environmental Engineering Domain, Transportation Engineering Domain, etc.), Mechanical Engineering Domain, and Electrical Engineering Domain.
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17 APPENDIX H ONTOLOGY EVALUATION QUESTIONNAIRE
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Appendix H: Ontology Evaluation Questionnaire 285
Appendix H: Ontology Evaluation Questionnaire 286
Appendix H: Ontology Evaluation Questionnaire 287
Appendix H: Ontology Evaluation Questionnaire
18 APPENDIX I CIKP FRAMEWORK EVALUATION QUESTIONNAIRE
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Appendix I: CIKP Framework Evaluation Questionnaire 290
Appendix I: CIKP Framework Evaluation Questionnaire 291
Appendix I: CIKP Framework Evaluation Questionnaire 292
Appendix I: CIKP Framework Evaluation Questionnaire
19 APPENDIX J EXAMPLE OF SWRL RULE VERIFICATION
This is an example of using Jess to verify rules written in SWRL. The rule to be verified says that “if there is a change order notice issued by a professional individual (such as a designer) regarding a certain project in a specific subject domain, the contractor of that project in the same subject domain should get this change order notice.” This is an example to handle unknown unknowns by the CIKP system. If a project contractor did not subscribe to change order notices of the project that he/she is working on, by having this rule, the system would automatically subscribe to that information on behalf of the contractor. This rule is written in formal SWRL rule, as shown in Figure J-1.
Change_Order_Notice(?x) ∧ has_Knowledge_Item_Source(?x, ?y) ∧ Professional_Individual(?y) ∧
has_Subject_Domain(?y, ?c) ∧ has_Project_Affiliation(?x, ?z) ∧ Project(?z) ∧ Actor(?a) ∧
play_Role(?a, ?b) ∧ Project_Contractor(?b) ∧ has_Subject_Domain(?a, ?c) ∧
has_Project_Affiliation(?b, ?z) → has_Information_Need(?a, ?x)
Figure J-1: SWRL Rule Example
In order to verify this rule, several instances have been created. The first instance is an instance of the class Electrical Engineer, who is the one to issue a change order notice. Figure J-2 shows all attributes that are required to perform the verification. The attribute has_Subject_Domain is required by this rule, others are mandatory attributes required by the class Electrical Engineer. This electrical engineer John Woo has a subject domain Electrical Engineering.
The second instance created is an instance of the class Change Order Notice, which has a
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name Change_Order_Notice_1 in the ontology. Figure J-3 shows all required attributes of this item. Two attributes are required by this SWRL rule: has_Knowledge_Item_Source (value: John Woo) and has_Project_Affiliation (value: Project_ABC). Other attributes are mandatory for any instance of the class Change Order Notice.
The third instance created is an instance of the class Electrical and Wiring Contracting Company, which has a name Company_XYZ in the ontology. Figure J-4 shows all attributes asserted by the system user. Two attributes required by this example are play_Role (value: Project_Specialty_Contractor_13) and has_Subject_Domain (value: Electrical Engineering). Other attributes are required by the class Electrical and Wiring Contracting Company. It should be noted that there is no any value currently asserted for the attribute has_Information_Need (see red box in Figure J-4).
Other instances involved in this example include a project (Project_ABC) and a role (Project_Specialty_Contractor_13) which has an attribute has_Project_Affiliation (value: Project_ABC).
After the rule is coded in the SWRL Tab of Protégé and the Jess is loaded, by pushing the “OWL+SWRLÆJess” button in Figure J-6, OWL ontology and SWRL rules are imported to the Jess reasoning engine. In order to run the reasoning engine, one needs to push the “Run Jess” button in Figure J-6 to check if any new fact could be generated. Finally by pushing the “JessÆOWL” button in Figure J-6, new assertions are brought back to the OWL file and then displayed in Protégé-OWL interface.
After running the Jess reasoning engine in this example, a new assertion is generated which claims that the instance Company_XYZ has an attribute has_Information_Need (value: Change_Order_Notice_1). Figure J-5 shows the new attribute value in red box. This example
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verified the SWRL rule indicated in Figure J-1. Other SWRL rules have been examined in a similar approach.
It should be noted that in this example, John Woo is an instance of Electrical Engineer but the SWRL rule is about a Professional Individual. Also, Company_XYZ plays a role of Project Specialty Contractor but the SWRL rule is about a Project Contractor. The fact is that this rule is well conducted in this example. The reason is that the OWL ontology has already recognized that an Electrical Engineer is a Professional Individual and a Project Specialty Contractor is a Project Contractor.
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Figure J-2: Instance of Electrical Engineer
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Figure J-3: Instance of Change Order Notice
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Figure J-4: Instance of Electrical and Wiring Contracting Company (Before Reasoning)
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Figure J-5: Instance of Electrical and Wiring Contracting Company (After Reasoning)
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Figure J-6: SWRL Rule and Jess Reasoning Engine
Appendix J: Example of SWRL Rule Verification