DOCSLIB.ORG

Ontology for Information Systems (O4IS) Design Methodology Conceptualizing, Designing and Representing Domain Ontologies

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Ontology for Information Systems (O4IS) Design Methodology Conceptualizing, Designing and Representing Domain Ontologies Ontology for Information Systems (O4IS) Design Methodology Conceptualizing, Designing and Representing Domain Ontologies Vandana Kabilan October 2007. A Dissertation submitted to The Royal Institute of Technology in partial fullfillment of the requirements for the degree of Doctor of Technology . The Royal Institute of Technology School of Information and Communication Technology Department of Computer and Systems Sciences IV DSV Report Series No. 07–013 ISBN 978–91–7178–752–1 ISSN 1101–8526 ISRN SU–KTH/DSV/R– –07/13– –SE V All knowledge that the world has ever received comes from the mind; the infinite library of the universe is in our own mind. – Swami Vivekananda. (1863 – 1902) Indian spiritual philosopher. The whole of science is nothing more than a refinement of everyday thinking. – Albert Einstein (1879 – 1955) German-Swiss-U.S. scientist. Science is a mechanism, a way of trying to improve your knowledge of na- ture. It’s a system for testing your thoughts against the universe, and seeing whether they match. – Isaac Asimov. (1920 – 1992) Russian-U.S. science-fiction author. VII Dedicated to the three KAs of my life: Kabilan, Rithika and Kavin. IX Abstract. Globalization has opened new frontiers for business enterprises and human com- munication. There is an information explosion that necessitates huge amounts of informa- tion to be speedily processed and acted upon. Information Systems aim to facilitate human decision-making by retrieving context-sensitive information, making implicit knowledge ex- plicit and to reuse the knowledge that has already been discovered. A possible answer to meet these goals is the use of Ontology. Ontology has been studied for a long time in the fields of AI, Logic and Linguistics. Cur- rent state-of-the art research in Information Systems has focused on the use of ontologies. However, there remain many obstacles for the practical and commercial use of ontologies for Information Systems. One such obstacle is that current Information System designers lack the know-how to successfully design an ontology. Current ontology design methodologies are difficult to use by Information Systems designers having little theoretical knowledge of ontol- ogy modeling. Another issue is that business enterprises mostly function in the social domain where there are complex underlying semantics and pragmatics involved. This research tries to solve some of these issues by proposing the Ontology for Information Systems (O4IS) Design Methodology for the design of ontologies for Information Systems. The research also proposes a Unified Semantic Procedural Pragmatic Design for explicit conceptu- alization of the semantics and pragmatics of a domain. We further propose a set of Semantic Analysis Representations as conceptual analysis patterns for semantic relationship identifica- tion. We also put forward the Dual Conceptual Representation so that the designed ontology is understandable by both humans and machines. Finally, a logical architecture for domain ontology design called the Multi-Tier Domain Ontology Architecture is proposed. We follow the design science in Information Systems research methodology. The proposed solutions are demonstrated through two case studies carried out in different domains. The first case study is that of business contract knowledge management, which focuses on the analysis of contractual obligations, their fulfillment via the performance of business actions, and the deduction of a contract compliant workflow model. The second case study relates to military operations simulations and modeling. The emphasis in this case study is to analyze, model and represent the domain knowledge as a re-usable resource to be used in a number of modeling and simulation applications. XI Acknowledgement. It has been an enlightening journey into unknown frontiers, learning to solve new challenges, and finding more pieces of knowledge scattered along the way. But it would not have been possible, if not for the gentle steering and supportive guidance of my supervisor, Prof. Paul Johannesson. I thank – Paul; For being an quintessential supervisor who like a compass activates the magnets of curiosity, knowledge and wisdom. I thank all my colleagues at DSV – Maria, Jelena, Birger, Hercules and everyone else at SYSLAB; For being such excellent listeners, contributors and critics of my theories. I also acknowledge my colleagues at the Swedish Defence Research Agency – Vahid, Mar- ianela and Pernilla; For giving me the opportunity to be a part of their team and for the countless hours of brainstorming. I acknowledge all the wonderful researchers with whom I have had the opportunity to meet and exchange ideas. I specially thank Hans Weigand for his collaboration and construc- tive criticisms. Last, but not the least, this research would never have become a reality, without the sup- port and motivation provided by my family – Mama and Athai; For your constant inspiration and motivation. Mom and Dad; For providing me with the best possible start to life that I could have ever wished for. Priya; For the being the perfect sister and professional proof-reader. Special thanks to my children Rithika and Kavin whose innocent smiles wipe away all exhaustion at the end of each tiring day. Finally, I owe this PhD to the one person who has believed in me always, pushed me harder, to reach higher – my best friend, my love and husband, Kabilan. Thank you for truly shining like the North Star, eternally constant without a flicker. Contents 1 Introduction ::::::::::::::::::::::::::::::::::::::::::::::::::::: 1 1.1 Research Fields . .1 1.1.1 Information Systems . .2 1.1.2 Ontologies for Information Systems . .3 1.1.3 Conceptual Modeling and Ontology . .5 1.2 Research Domain . .7 1.3 Research Motivation: Information systems need ontology . .7 1.4 Research Problems . .9 1.5 Research Questions . 12 1.6 Goal........................................................ 13 1.7 Purpose . 15 1.8 Scope . 15 1.9 Research Methodology . 15 1.10 Research Evaluation Criteria . 18 1.11 Case Studies . 19 1.12 Results . 20 1.13 Disposition . 24 1.14 Publications . 26 2 Knowledge Management and Information Systems :::::::::::::::::: 31 2.1 The Data-Information-Knowledge-Wisdom Model . 31 2.2 Information Systems . 33 2.2.1 Knowledge Base Versus Database Information Systems. 34 2.2.2 Knowledge Bases and Knowledge Base Systems . 35 2.3 Knowledge Management . 36 2.3.1 Knowledge Representation . 38 2.3.2 Different Roles of Knowledge Representation . 38 2.4 Relevance of Chapter . 40 XIV Contents 3 Ontology and Information Systems :::::::::::::::::::::::::::::::: 41 3.1 Ontology . 41 3.2 Uses of Ontologies . 44 3.3 Ontology – A Historical Background . 45 3.3.1 From Philosophy, AI and Logic . 45 3.3.2 From Linguistics to Information Systems . 47 3.3.3 From Knowledge Management to Information Systems . 48 3.3.4 What Information Systems can learn from Philosophical Ontology . 50 3.4 Ontology Types. 52 3.5 Ontology Architectures . 53 3.6 Ontology Design Principles . 54 3.7 Ontology Development Approaches . 54 3.8 Ontology Design Methodologies . 56 3.8.1 Uschold and Gruninger’s Skeletal Method . 57 3.8.2 Gruninger and Fox Method . 57 3.8.3 Noy and McGuinness Method . 59 3.8.4 UPON. 61 3.8.5 METHONTOLOGY . 62 3.8.6 Conceptualization in Methontology . 64 3.9 Relevance of Chapter . 65 4 Conceptual Modeling ::::::::::::::::::::::::::::::::::::::::::::: 67 4.1 Conceptual Model . 68 4.2 Conceptual Modeling Methods . 69 4.2.1 ER Modeling Revisited . 71 4.2.2 Conceptual Graphs . 73 4.3 Intensional and Extensional Conceptual Modeling . 74 4.3.1 Conceptual Modeling Languages. 75 4.4 Using Conceptual Modeling for Ontology Modeling. 75 4.5 Conceptual Modeling Patterns . 77 4.5.1 Design Patterns . 78 4.6 Procedural and Behavior Representation Languages . 82 4.7 Relevance of Chapter . 86 5 Ontology for Information Systems Design Methodology ::::::::::::: 89 5.1 Requirements on O4IS Design Methodology . 92 5.2 Introducing the O4IS Design Methodology . 93 5.2.1 Template for describing the O4IS Design Methodology . 95 5.2.2 G1: Establish the scope of the domain . 95 Contents XV 5.2.3 G2: Establish the targeted users, applications, and functional requirements . 96 5.2.4 G3: Choose ontology architecture: physical and logical . 98 5.2.5 G4: Choose ontology development approach . 99 5.2.6 G5: Choose level of ontology representation . 100 5.2.7 G6: Choose knowledge acquisition methods and tools . 101 5.2.8 G7: Knowledge analysis - conceptualize the domain ontology . 102 5.2.9 G8: Knowledge representation - implement the domain ontology . 102 5.2.10 G9: Evaluate, assess and verify the domain ontology . 103 5.2.11 G10: Use, maintain and manage the domain ontology . 104 5.3 Multi-tiered Domain Ontology Architecture . 105 5.3.1 Advantages of the Multi-tier Domain Ontology Architecture . 107 5.4 Dual Conceptual Representation . 108 5.4.1 UML for Conceptual Modeling and Ontology Representation . 110 6 Unified Semantic Procedural Pragmatic Design ::::::::::::::::::::: 113 6.1 Semantics, Procedures and Pragmatics . 115 6.2 Introducing the USP2 Design . 117 6.3 Verb Phrase Ontology . 119 6.4 Performative Verb Ontology . 122 6.5 Semantic Analysis Representations: Discovering Semantic Relationships . ..
Load more

Recommended publications
  • Information Technology Management 14
    Information Technology Management 14 Valerie Bryan Practitioner Consultants Florida Atlantic University Layne Young Business Relationship Manager Indianapolis, IN Donna Goldstein GIS Coordinator Palm Beach County School District Information Technology is a fundamental force in • IT as a management tool; reshaping organizations by applying investment in • understanding IT infrastructure; and computing and communications to promote competi- • ȱǯ tive advantage, customer service, and other strategic ęǯȱǻȱǯȱǰȱŗşşŚǼ ȱȱ ȱ¢ȱȱȱ¢ǰȱȱ¢Ȃȱ not part of the steamroller, you’re part of the road. ǻ ȱǼ ȱ ¢ȱ ȱ ȱ ęȱ ȱ ȱ ¢ǯȱȱȱȱȱ ȱȱ ȱ- ¢ȱȱȱȱȱǯȱ ǰȱ What is IT? because technology changes so rapidly, park and recre- ation managers must stay updated on both technological A goal of management is to provide the right tools for ȱȱȱȱǯ ěȱ ȱ ě¢ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱ ȱȱȱǰȱȱǰȱ ȱȱȱȱȱȱǯȱȱȱȱ ǰȱȱȱȱ ȱȱ¡ȱȱȱȱ recreation organization may be comprised of many of terms crucial for understanding the impact of tech- ȱȱȱȱǯȱȱ ¢ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ - ȱȱęȱȱȱ¢ȱȱȱȱ ¢ȱǻ Ǽȱȱȱȱ¢ȱ ȱȱȱȱ ǰȱ ȱ ȱ ȬȬ ȱ ȱ ȱ ȱȱǯȱȱȱȱȱ ¢ȱȱǯȱȱ ȱȱȱȱ ȱȱ¡ȱȱȱȱȱǻǰȱŗşŞśǼǯȱ ȱȱȱȱȱȱĞȱȱȱȱ ǻȱ¡ȱŗŚǯŗȱ Ǽǯ ě¢ȱȱȱȱǯ Information technology is an umbrella term Details concerning the technical terms used in this that covers a vast array of computer disciplines that ȱȱȱȱȱȱȬȬęȱ ȱȱ permit organizations to manage their information ǰȱ ȱ ȱ ȱ Ȭȱ ¢ȱ ǯȱ¢ǰȱȱ¢ȱȱȱ ȱȱ ǯȱȱȱȱȱȬȱ¢ȱ a fundamental force in reshaping organizations by applying ȱȱ ȱȱ ȱ¢ȱȱȱȱ investment in computing and communications to promote ȱȱ¢ǯȱ ȱȱȱȱ ȱȱ competitive advantage, customer service, and other strategic ȱ ǻȱ ȱ ŗŚȬŗȱ ęȱ ȱ DZȱ ęȱǻǰȱŗşşŚǰȱǯȱřǼǯ ȱ Ǽǯ ȱ¢ȱǻ Ǽȱȱȱȱȱ ȱȱȱęȱȱȱ¢ȱȱ ȱ¢ǯȱȱȱȱȱ ȱȱ ȱȱ ȱȱȱȱ ȱ¢ȱ ȱȱǯȱ ȱ¢ǰȱ ȱȱ ȱDZ ȱȱȱȱǯȱ ȱȱȱȱǯȱ It lets people learn things they didn’t think they could • ȱȱȱ¢ǵ ȱǰȱȱǰȱȱȱǰȱȱȱȱȱǯȱ • the manager’s responsibilities; ǻȱǰȱȱ¡ȱĜȱȱĞǰȱȱ • information resources; ǷȱǯȱȱȱřŗǰȱŘŖŖŞǰȱ • disaster recovery and business continuity; ȱĴDZȦȦ ǯ ǯǼ Information Technology Management 305 Exhibit 14.
    [Show full text]
  • Bioinformatics 1
    Bioinformatics 1 Bioinformatics School School of Science, Engineering and Technology (http://www.stmarytx.edu/set/) School Dean Ian P. Martines, Ph.D. ([email protected]) Department Biological Science (https://www.stmarytx.edu/academics/set/undergraduate/biological-sciences/) Bioinformatics is an interdisciplinary and growing field in science for solving biological, biomedical and biochemical problems with the help of computer science, mathematics and information technology. Bioinformaticians are in high demand not only in research, but also in academia because few people have the education and skills to fill available positions. The Bioinformatics program at St. Mary’s University prepares students for graduate school, medical school or entry into the field. Bioinformatics is highly applicable to all branches of life sciences and also to fields like personalized medicine and pharmacogenomics — the study of how genes affect a person’s response to drugs. The Bachelor of Science in Bioinformatics offers three tracks that students can choose. • Bachelor of Science in Bioinformatics with a minor in Biology: 120 credit hours • Bachelor of Science in Bioinformatics with a minor in Computer Science: 120 credit hours • Bachelor of Science in Bioinformatics with a minor in Applied Mathematics: 120 credit hours Students will take 23 credit hours of core Bioinformatics classes, which included three credit hours of internship or research and three credit hours of a Bioinformatics Capstone course. BS Bioinformatics Tracks • Bachelor of Science
    [Show full text]
  • A Framework for Comparing the Use of a Linguistic Ontology in an Application
    A Framework for Comparing the use of a Linguistic Ontology in an Application Julieanne van Zyl1 and Dan Corbett2 Abstract. A framework recently developed for understanding and “a body of knowledge about the world (or a domain) that a) is classifying ontology applications provides opportunities to review a repository of primitive symbols used in meaning the state of the art, and to provide guidelines for application b) organises these symbols in a tangled subsumption developers from different communities [1]. The framework hierarchy; and identifies four main categories of ontology applications: neutral c) further interconnects these symbols using a rich system of authoring, ontology as specification, common access to semantic and discourse-pragmatic relations defined among information, and ontology-based search. Specific scenarios are the concepts” [2]. described for each category and a number of features have been identified to highlight the similarities and differences between There has been some work on comparing ontology applications, them. In this paper we identify additional scenarios, describing the which refer to “the application that makes use of or benefits from use of a linguistic ontology in an application. We populate the the ontology (possibly directly)” [1]. A framework [3] was scenarios with a number of prominent research and industrial developed for comparing various projects in ontology design, applications from diverse communities such as knowledge-based which considered the differences and similarities in the way the machine translation, medical databases, heterogenous information projects treat some basic knowledge representation aspects. The systems integration, multilingual text generation, Web-based projects compared include three natural language ontologies, two applications, and information retrieval.
    [Show full text]
  • A Survey of Top-Level Ontologies to Inform the Ontological Choices for a Foundation Data Model
    A survey of Top-Level Ontologies To inform the ontological choices for a Foundation Data Model Version 1 Contents 1 Introduction and Purpose 3 F.13 FrameNet 92 2 Approach and contents 4 F.14 GFO – General Formal Ontology 94 2.1 Collect candidate top-level ontologies 4 F.15 gist 95 2.2 Develop assessment framework 4 F.16 HQDM – High Quality Data Models 97 2.3 Assessment of candidate top-level ontologies F.17 IDEAS – International Defence Enterprise against the framework 5 Architecture Specification 99 2.4 Terminological note 5 F.18 IEC 62541 100 3 Assessment framework – development basis 6 F.19 IEC 63088 100 3.1 General ontological requirements 6 F.20 ISO 12006-3 101 3.2 Overarching ontological architecture F.21 ISO 15926-2 102 framework 8 F.22 KKO: KBpedia Knowledge Ontology 103 4 Ontological commitment overview 11 F.23 KR Ontology – Knowledge Representation 4.1 General choices 11 Ontology 105 4.2 Formal structure – horizontal and vertical 14 F.24 MarineTLO: A Top-Level 4.3 Universal commitments 33 Ontology for the Marine Domain 106 5 Assessment Framework Results 37 F. 25 MIMOSA CCOM – (Common Conceptual 5.1 General choices 37 Object Model) 108 5.2 Formal structure: vertical aspects 38 F.26 OWL – Web Ontology Language 110 5.3 Formal structure: horizontal aspects 42 F.27 ProtOn – PROTo ONtology 111 5.4 Universal commitments 44 F.28 Schema.org 112 6 Summary 46 F.29 SENSUS 113 Appendix A F.30 SKOS 113 Pathway requirements for a Foundation Data F.31 SUMO 115 Model 48 F.32 TMRM/TMDM – Topic Map Reference/Data Appendix B Models 116 ISO IEC 21838-1:2019
    [Show full text]
  • How to Keep a Knowledge Base Synchronized with Its Encyclopedia Source
    Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) How to Keep a Knowledge Base Synchronized with Its Encyclopedia Source Jiaqing Liang12, Sheng Zhang1, Yanghua Xiao134∗ 1School of Computer Science, Shanghai Key Laboratory of Data Science Fudan University, Shanghai, China 2Shuyan Technology, Shanghai, China 3Shanghai Internet Big Data Engineering Technology Research Center, China 4Xiaoi Research, Shanghai, China [email protected], fshengzhang16,[email protected] Abstract However, most of these knowledge bases tend to be out- dated, which limits their utility. For example, in many knowl- Knowledge bases are playing an increasingly im- edge bases, Donald Trump is only a business man even af- portant role in many real-world applications. How- ter the inauguration. Obviously, it is important to let ma- ever, most of these knowledge bases tend to be chines know that Donald Trump is the president of the United outdated, which limits the utility of these knowl- States so that they can understand that the topic of an arti- edge bases. In this paper, we investigate how to cle mentioning Donald Trump is probably related to politics. keep the freshness of the knowledge base by syn- Moreover, new entities are continuously emerging and most chronizing it with its data source (usually ency- of them are popular, such as iphone 8. However, it is hard for clopedia websites). A direct solution is revisiting a knowledge base to cover these entities in time even if the the whole encyclopedia periodically and rerun the encyclopedia websites have already covered them. entire pipeline of the construction of knowledge freshness base like most existing methods.
    [Show full text]
  • Artificial Consciousness and the Consciousness-Attention Dissociation
    Consciousness and Cognition 45 (2016) 210–225 Contents lists available at ScienceDirect Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog Review article Artificial consciousness and the consciousness-attention dissociation ⇑ Harry Haroutioun Haladjian a, , Carlos Montemayor b a Laboratoire Psychologie de la Perception, CNRS (UMR 8242), Université Paris Descartes, Centre Biomédical des Saints-Pères, 45 rue des Saints-Pères, 75006 Paris, France b San Francisco State University, Philosophy Department, 1600 Holloway Avenue, San Francisco, CA 94132 USA article info abstract Article history: Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to Received 6 July 2016 implement sophisticated forms of human intelligence in machines. This research attempts Accepted 12 August 2016 to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable Keywords: once we overcome short-term engineering challenges. We believe, however, that phenom- Artificial intelligence enal consciousness cannot be implemented in machines. This becomes clear when consid- Artificial consciousness ering emotions and examining the dissociation between consciousness and attention in Consciousness Visual attention humans. While we may be able to program ethical behavior based on rules and machine Phenomenology learning, we will never be able to reproduce emotions or empathy by programming such Emotions control systems—these will be merely simulations. Arguments in favor of this claim include Empathy considerations about evolution, the neuropsychological aspects of emotions, and the disso- ciation between attention and consciousness found in humans.
    [Show full text]
  • Functions in Basic Formal Ontology
    Applied Ontology 11 (2016) 103–128 103 DOI 10.3233/AO-160164 IOS Press Functions in Basic Formal Ontology Andrew D. Spear a,∗, Werner Ceusters b and Barry Smith c a Philosophy Department, Grand Valley State University, 1 Campus Drive, Allendale, MI 49401, USA Tel.: +1 616 331 2847; Fax: +1 616 331 2601; E-mail: [email protected] b Departments of Biomedical Informatics and Psychiatry, University at Buffalo, 77 Goodell street, Buffalo, NY 14203, USA E-mail: [email protected] c Department of Philosophy, University at Buffalo, 135 Park Hall, Buffalo, NY 14260, USA E-mail: [email protected] Abstract. The notion of function is indispensable to our understanding of distinctions such as that between being broken and being in working order (for artifacts) and between being diseased and being healthy (for organisms). A clear account of the ontology of functions and functioning is thus an important desideratum for any top-level ontology intended for application to domains such as engineering or medicine. The benefit of using top-level ontologies in applied ontology can only be realized when each of the categories identified and defined by a top-level ontology is integrated with the others in a coherent fashion. Basic Formal Ontology (BFO) has from the beginning included function as one of its categories, exploiting a version of the etiological account of function that is framed at a level of generality sufficient to accommodate both biological and artifactual functions. This account has been subjected to a series of criticisms and refinements. We here articulate BFO’s account of function, provide some reasons for favoring it over competing views, and defend it against objections.
    [Show full text]
  • Description Logics Emerge from Ivory Towers Deborah L
    Description Logics Emerge from Ivory Towers Deborah L. McGuinness Stanford University, Stanford, CA, 94305 [email protected] Abstract: Description logic (DL) has existed as a field for a few decades yet somewhat recently have appeared to transform from an area of academic interest to an area of broad interest. This paper provides a brief historical perspective of description logic developments that have impacted their usability beyond just in universities and research labs and provides one perspective on the topic. Description logics (previously called terminological logics and KL-ONE-like systems) started with a motivation of providing a formal foundation for semantic networks. The first implemented DL system – KL-ONE – grew out of Brachman’s thesis [Brachman, 1977]. This work was influenced by the work on frame systems but was focused on providing a foundation for building term meanings in a semantically meaningful and unambiguous manner. It rejected the notion of maintaining an ever growing (seemingly adhoc) vocabulary of link and node names seen in semantic networks and instead embraced the notion of a fixed set of domain-independent “epistemological primitives” that could be used to construct complex, structured object descriptions. It included constructs such as “defines-an-attribute-of” as a built-in construct and expected terms like “has-employee” to be higher-level terms built up from the epistemological primitives. Higher level terms such as “has-employee” and “has-part-time-employee” could be related automatically based on term definitions instead of requiring a user to place links between them. In its original incarnation, this led to maintaining the motivation of semantic networks of providing broad expressive capabilities (since people wanted to be able to represent natural language applications) coupled with the motivation of providing a foundation of building blocks that could be used in a principled and well-defined manner.
    [Show full text]
  • Learning Ontologies from RDF Annotations
    /HDUQLQJÃRQWRORJLHVÃIURPÃ5')ÃDQQRWDWLRQV $OH[DQGUHÃ'HOWHLOÃ&DWKHULQHÃ)DURQ=XFNHUÃ5RVHÃ'LHQJ ACACIA project, INRIA, 2004, route des Lucioles, B.P. 93, 06902 Sophia Antipolis, France {Alexandre.Delteil, Catherine.Faron, Rose.Dieng}@sophia.inria.fr $EVWUDFW objects, as in [Mineau, 1990; Carpineto and Romano, 1993; Bournaud HWÃDO., 2000]. In this paper, we present a method for learning Since all RDF annotations are gathered inside a common ontologies from RDF annotations of Web RDF graph, the problem which arises is the extraction of a resources by systematically generating the most description for a given resource from the whole RDF graph. specific generalization of all the possible sets of After a brief description of the RDF data model (Section 2) resources. The preliminary step of our method and of RDF Schema (Section 3), Section 4 presents several consists in extracting (partial) resource criteria for extracting partial resource descriptions. In order descriptions from the whole RDF graph gathering to deal with the intrinsic complexity of the building of a all the annotations. In order to deal with generalization hierarchy, we propose an incremental algorithmic complexity, we incrementally build approach by gradually increasing the size of the descriptions the ontology by gradually increasing the size of the resource descriptions we consider. we consider. The principle of the approach is explained in Section 5 and more deeply detailed in Section 6. Ã ,QWURGXFWLRQ Ã 7KHÃ5')ÃGDWDÃPRGHO The Semantic Web, expected to be the next step that will RDF is the emerging Web standard for annotating resources lead the Web to its full potential, will be based on semantic HWÃDO metadata describing all kinds of Web resources.
    [Show full text]
  • A Translation Approach to Portable Ontology Specifications
    Knowledge Systems Laboratory September 1992 Technical Report KSL 92-71 Revised April 1993 A Translation Approach to Portable Ontology Specifications by Thomas R. Gruber Appeared in Knowledge Acquisition, 5(2):199-220, 1993. KNOWLEDGE SYSTEMS LABORATORY Computer Science Department Stanford University Stanford, California 94305 A Translation Approach to Portable Ontology Specifications Thomas R. Gruber Knowledge System Laboratory Stanford University 701 Welch Road, Building C Palo Alto, CA 94304 [email protected] Abstract To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions, and other objects — is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how
    [Show full text]
  • Computability and Complexity
    Computability and Complexity Lecture Notes Herbert Jaeger, Jacobs University Bremen Version history Jan 30, 2018: created as copy of CC lecture notes of Spring 2017 Feb 16, 2018: cleaned up font conversion hickups up to Section 5 Feb 23, 2018: cleaned up font conversion hickups up to Section 6 Mar 15, 2018: cleaned up font conversion hickups up to Section 7 Apr 5, 2018: cleaned up font conversion hickups up to the end of these lecture notes 1 1 Introduction 1.1 Motivation This lecture will introduce you to the theory of computation and the theory of computational complexity. The theory of computation offers full answers to the questions, • what problems can in principle be solved by computer programs? • what functions can in principle be computed by computer programs? • what formal languages can in principle be decided by computer programs? Full answers to these questions have been found in the last 70 years or so, and we will learn about them. (And it turns out that these questions are all the same question). The theory of computation is well-established, transparent, and basically simple (you might disagree at first). The theory of complexity offers many insights to questions like • for a given problem / function / language that has to be solved / computed / decided by a computer program, how long does the fastest program actually run? • how much memory space has to be used at least? • can you speed up computations by using different computer architectures or different programming approaches? The theory of complexity is historically younger than the theory of computation – the first surge of results came in the 60ties of last century.
    [Show full text]
  • Framework for Formal Ontology Barry Smith and Kevin Mulligan
    Framework for Formal Ontology Barry Smith and Kevin Mulligan NOTICE: THIS MATERIAL M/\Y BE PROTECTED BY COPYRIGHT LAW (TITLE 17, u.s. CODE) ABSTRACT. The discussions which follow rest on a distinction, or object-relations in the world; nor does it concern itself rust expounded by Husser!, between formal logic and formal specifically with sentences about such objects. It deals, ontology. The former concerns itself with (formal) meaning-struc­ rather, with sentences in general (including, for example, tures; the latter with formal structures amongst objects and their the sentences of mathematics),2 and where it is applied to parts. The paper attempts to show how, when formal ontological considerations are brought into play, contemporary extensionalist sentences about objects it can take no account of any theories of part and whole, and above all the mereology of Lesniew­ formal or material object·structures which may be ex­ ski, can be generalised to embrace not only relations between con­ hibited amongst the objects pictured. Its attentions are crete objects and object-pieces, but also relations between what we directed, rather, to the relations which obtain between shall call dependent parts or moments. A two-dimensional formal sentences purely in virtue of what we can call their logical language is canvassed for the resultant ontological theory, a language which owes more to the tradition of Euler, Boole and Venn than to complexity (for example the deducibility-relations which the quantifier-centred languages which have predominated amongst obtain between any sentence of the form A & Band analytic philosophers since the time of Frege and Russell.
    [Show full text]