DOCSLIB.ORG

Ontology Representation : Design Patterns and Ontologies That Make Sense

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Ontology Representation : Design Patterns and Ontologies That Make Sense UvA-DARE (Digital Academic Repository) Ontology Representation : design patterns and ontologies that make sense Hoekstra, R.J. Publication date 2009 Document Version Final published version Link to publication Citation for published version (APA): Hoekstra, R. J. (2009). Ontology Representation : design patterns and ontologies that make sense. IOS Press. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:24 Sep 2021 Ontology Representation Design Patterns and Ontologies that Make Sense Rinke Hoekstra SIKS Dissertation Series No. 2009-15 The research reported in this thesis has been carried out under the auspices of SIKS, the Dutch Research School for Information and Knowledge Systems. ONTOLOGY REPRESENTATION Design Patterns and Ontologies that Make Sense ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof. dr. D.C. van den Boom ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op vrijdag 18 september 2009, te 12:00 uur door Rinke Jan Hoekstra geboren te Zaanstad Promotiecommissie Promotor: prof. dr. J.A.P.J. Breuker Copromotores: prof. dr. T.M. van Engers dr. R.G.F. Winkels Overige leden: prof. dr. F. van Harmelen prof. dr. E. Motta prof. dr. F.J.M.M. Veltman prof. dr. B.J. Wielinga dr. B. Bredeweg Faculteit der Rechtsgeleerdheid To Eefje and Lieve Contents 1 Introduction1 1.1 Introduction..............................1 1.2 Questions...............................3 1.3 Ontologies...............................4 1.4 Contribution and Overview.....................6 2 Knowledge Representation8 2.1 Introduction..............................8 2.2 Two Schools..............................9 2.2.1 Intelligence by First Principles............... 10 2.2.2 Production Systems..................... 12 2.2.3 Semantic Networks...................... 14 2.2.4 Frames............................. 15 2.2.5 Frame Languages....................... 17 2.3 Epistemology............................. 19 2.3.1 Knowledge and Representation............... 20 2.3.2 Representation and Language................ 22 2.3.3 Adequacy........................... 26 2.4 Components of a Knowledge Based System............ 28 2.4.1 Modelling Principles..................... 28 2.5 Knowledge Representation..................... 32 2.5.1 Description Logics...................... 33 2.6 Discussion............................... 36 3 Semantic Web 39 3.1 Introduction.............................. 39 3.1.1 The Web............................ 40 3.2 Groundwork.............................. 41 3.3 Lightweight Semantics........................ 43 3.3.1 RDF: The Resource Description Framework........ 44 3.3.2 The RDF Schema Vocabulary Description Language... 47 3.4 Requirements for Web-Based Knowledge Representation.... 50 3.5 The Web Ontology Language.................... 52 3.5.1 OWL.............................. 52 3.5.2 OWL 2............................. 57 3.6 Discussion............................... 63 Contents v 4 Ontologies 66 4.1 Introduction.............................. 66 4.2 Ontologies as Artefacts........................ 67 4.3 Ontology in Philosophy....................... 73 4.3.1 Problems in Formal Ontology: Semantics and Syntax.. 74 4.4 Two Kinds of Ontologies....................... 76 4.5 Discussion............................... 79 5 Ontology Engineering 82 5.1 Introduction.............................. 82 5.2 Criteria and Methodology...................... 83 5.2.1 A Social Activity?....................... 86 5.3 Categories and Structure....................... 88 5.3.1 A Basic Level?......................... 89 5.3.2 Beyond Categories...................... 91 5.4 Ontology Integration......................... 91 5.4.1 Types of Ontologies: Limits to Reuse............ 94 5.4.2 Safe Reuse........................... 97 5.4.3 Possibilities for Reuse.................... 101 5.5 Ontological Principles........................ 102 5.5.1 OntoClean........................... 103 5.5.2 Frameworks and Ontologies................ 106 5.6 Design Patterns............................ 111 5.6.1 Patterns and Metaphors................... 112 5.6.2 Patterns in Ontology Engineering............. 113 5.6.3 Knowledge Patterns..................... 114 5.6.4 Ontology Design Patterns.................. 115 5.7 Discussion............................... 118 6 Commonsense Ontology 120 6.1 Introduction.............................. 120 6.1.1 A Functional View...................... 122 6.1.2 Purpose............................ 124 6.2 Developing a Core Ontology.................... 127 6.2.1 Ontology Integration..................... 127 6.2.2 Ontology Reuse........................ 130 6.2.3 Scope.............................. 134 6.3 Ontology Modules.......................... 135 6.3.1 First Things First: The top-level............... 137 6.3.2 The Intentional Level..................... 139 6.3.3 The Legal Level........................ 142 6.4 Discussion............................... 143 7 Design Patterns 146 7.1 Introduction.............................. 146 7.1.1 Dealing with Models..................... 147 7.1.2 Structured Concepts..................... 150 7.1.3 Three Patterns......................... 151 7.2 Grasping the Diamond: The Reciprocity of Exchange ........ 152 7.2.1 Representing Transaction.................. 154 7.2.2 Discussion........................... 158 7.3 Reification and Summarisation: Relations and Social Reality ... 159 7.3.1 N-Ary Relations....................... 159 7.3.2 Social Reality......................... 162 7.3.3 Representing Summarisation................ 164 7.3.4 Discussion and Examples.................. 169 7.4 Sequences: Change and Causation .................. 175 7.4.1 Causation........................... 177 7.4.2 Representing Causal Change................ 180 7.4.3 Discussion........................... 185 7.5 Patterns in Practice: Action! ..................... 189 7.5.1 Representing Action..................... 190 7.5.2 Discussion........................... 194 7.6 Discussion............................... 194 8 Conclusion 198 8.1 Introduction.............................. 198 8.2 Compatibility of Ontologies..................... 199 8.3 Quality and Design.......................... 202 8.4 Rationale and Expressiveness of OWL 2.............. 204 8.5 Closing Remarks........................... 205 Bibliography 208 Chapter 1 Introduction “I pretty much try to stay in a constant state of confusion just because of the expression it leaves on my face.” Johnny Depp 1.1 Introduction An intelligent computer system needs to maintain an internal representation of that part of reality it is to be intelligent about: it needs to understand its domain. This understanding is commonly captured in models on the basis of which a computer can perform automated reasoning. For instance, a suitable model of ‘table’ will allow a machine to infer that each occurrence of a table has four legs. Models are often not a direct reflection of the domain itself, but rather represent our knowledge of that domain. The construction of models thus involves a mapping – a translation – between human understanding and that of a computer. In fact, the translation of human knowledge to computer models is one of the most profound problems in Artificial Intelligence (Newell, 1982). Over the course of several years, I have worked on many projects that in- volved the design and construction of such models, for a variety of purposes. The specification of an ontology for a large insurance company in the CLIME project (Boer et al., 2001; Winkels et al., 2002), made me aware that not only this modelling is a lot of work, but also that mistakes are easily made (Hoek- stra, 2001).1 The systematic representation of the contents of regulations in the E-POWER project (van Engers et al., 2000), illustrated not just the benefits of this approach, but simultaneously highlighted the enormous effort and scru- 1CLIME, Cooperative Legal Information Management and Explanation, Esprit Project 25414. 1 1.1. Introduction 2 tiny required from the people that actually build the models.2 Furthermore, both the E-POWER and KDE projects (Jansweijer et al., 2001) made it acutely clear how important the language is in which the model is expressed.3 Coming from academia, it was puzzling to see how easily one defaults to inexpressive but intuitive languages (or even just database tables), rather than well-wrought languages that support reasoning. What is more, it was very difficult to defend a more principled approach as available languages did not have widespread tool support (yet), nor did they offer even the trivial
Load more

Recommended publications
  • The Semantic Web and Its Languages Editor: Dieter Fensel Vrije Universiteit Amsterdam [email protected]
    TRENDS & CONTROVERSIES The semantic Web and its languages Editor: Dieter Fensel Vrije Universiteit Amsterdam [email protected] The Web has drastically changed the availability of electronic informa- epistemologically rich modeling primitives, provided by the frame com- tion, but its success and exponential growth have made it increasingly munity; formal semantics and efficient reasoning support, provided by difficult to find, access, present, and maintain such information for a wide description logics; and a standard proposal for syntactical exchange nota- variety of users. In reaction to this bottleneck, many new research initia- tions, provided by the Web community. (See the essay by Frank van tives and commercial enterprises have been set up to enrich available Harmelen and Ian Horrocks.) information with machine-processable semantics. Such support is essen- Another candidate for such a Web-based ontology modeling lan- tial for bringing the Web to its full potential in areas such as knowledge guage is DAML-ONT. The DARPA Agent Markup Language is a management and electronic commerce. This semantic Web will provide major, well-funded initiative, aimed at joining the many ongoing intelligent access to heterogeneous and distributed information, enabling semantic Web efforts and focused on bringing ontologies to the Web. software products (agents) to mediate between user needs and available The DAML language inherits many aspects from OIL, and the capabili- information sources. Early steps in the direction of a semantic Web were ties of the two languages are relatively similar. Both initiatives cooper- SHOE1 and later Ontobroker,2 but now many more projects exist. ate in a Joint EU/US ad hoc Agent Markup Language Committee to Originally, the Web grew mainly around HTML, which provided a achieve a joined language proposal.
    [Show full text]
  • Bi-Directional Transformation Between Normalized Systems Elements and Domain Ontologies in OWL
    Bi-directional Transformation between Normalized Systems Elements and Domain Ontologies in OWL Marek Suchanek´ 1 a, Herwig Mannaert2, Peter Uhnak´ 3 b and Robert Pergl1 c 1Faculty of Information Technology, Czech Technical University in Prague, Thakurova´ 9, Prague, Czech Republic 2Normalized Systems Institute, University of Antwerp, Prinsstraat 13, Antwerp, Belgium 3NSX bvba, Wetenschapspark Universiteit Antwerpen, Galileilaan 15, 2845 Niel, Belgium Keywords: Ontology, Normalized Systems, Transformation, Model-driven Development, Ontology Engineering, Software Modelling. Abstract: Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed.
    [Show full text]
  • Uluslararası Ders Kitapları Ve Eğitim Materyalleri Dergisi
    Uluslararası Ders Kitapları ve Eğitim Materyalleri Dergisi The Effect of Artifiticial Intelligence on Society and Artificial Intelligence the View of Artificial Intelligence in the Context of Film (I.A.) İpek Sucu İstanbul Gelişim Üniversitesi, Reklam Tasarımı ve İletişim Bölümü ABSTRACT ARTICLE INFO Consumption of produced, quick adoption of discovery, parallel to popularity, our interest in new and different is at the top; We live in the age of technology. A sense of wonder and satisfaction that mankind has existed in all ages throughout human history; it was the key to discoveries and discoveries. “Just as the discovery of fire was the most important invention in the early ages, artificial intelligence is also the most important project of our time.” (Aydın and Değirmenci, 2018: 25). It is the nature of man and the nearby brain. It is Z Artificial Intelligence ”technology. The concept of artificial intelligence has been frequently mentioned recently. In fact, I believe in artificial intelligence, the emergence of artificial intelligence goes back to ancient times. Various artificial intelligence programs have been created and robots have started to be built depending on the technological developments. The concepts such as deep learning and natural language processing are also on the agenda and films about artificial intelligence. These features were introduced to robots and the current concept of “artificial intelligence was reached. In this study, the definition, development and applications of artificial intelligence, the current state of artificial intelligence, the relationship between artificial intelligence and new media, the AI Artificial Intelligence (2001) film will be analyzed and evaluated within the scope of the subject and whether the robots will have certain emotions like people.
    [Show full text]
  • Automated Ontology Creation Using XML Schema Elements
    Automated Ontology Creation using XML Schema Elements Samuel Suhas Singapogu, Paulo C. G. Costa, J. Mark Pullen C4I center, George Mason University Fairfax, VA, USA [ssingapo,pcosta,mpullen]@c4i.gmu.edu Abstract—Ontologies are commonly used to represent formal semantics in a computer system, usually capturing them in the Although ontologies are valuable assets in system model- form of concepts, relationships and axioms. Axioms convey ing, testing and analysis, the process of manually creating an asserted knowledge and support inferring new knowledge ontology for a complex system is inherently tedious and through logical reasoning. For complex systems, the process of error prone. Existing methods of knowledge discovery and creating ontologies manually can be tedious and error-prone. ontology creation usually are based on text mining of a data Many automated methods of knowledge discovery are based on mining domain text corpus, but current state-of-the-art meth- corpus for that domain. XML-based systems capture the ods using this approach fail to consider properly semantic data structure and syntax of all necessary and meaningful ele- embedded in XML schemata in complex systems. This paper ments in a XML schema, which therefore is a useful starting proposes a mapping method for identifying relevant semantic point for semantic analysis. In such systems, XML schemata data in XML schemata, automatically structuring and repre- have been shown to be a valuable resource of semantic data senting it in the form of a draft ontology. Concepts, concept [2]. Command and Control systems in the military context hierarchy and domain relationships from XML schema are support various functions, including commanding of forces mapped to relevant parts of an OWL ontology.
    [Show full text]
  • Machine Learning
    Graham Capital Management Research Note, September 2017 Machine Learning Erik Forseth1, Ed Tricker2 Abstract Machine learning is more fashionable than ever for problems in data science, predictive modeling, and quantitative asset management. Developments in the field have revolutionized many aspects of modern life. Nevertheless, it is sometimes difficult to understand where real value ends and speculative hype begins. Here we attempt to demystify the topic. We provide a historical perspective on artificial intelligence and give a light, semi-technical overview of prevailing tools and techniques. We conclude with a brief discussion of the implications for investment management. Keywords Machine learning, AI 1Quantitative Researcher 2Head of Research and Development 1. Introduction by the data they are fed—which attempt to find a solution to some mathematical optimization problem. There remains a Machine Learning is the study of computer algorithms that vast gulf between the space of tasks which can be formulated improve automatically through experience. in this way, and the space of tasks that require, for example, – Tom Mitchell (1997) reasoning or abstract planning. There is a fundamental divide Machine Learning (ML) has emerged as one of the most between the capability of a computer model to map inputs to exciting areas of research across nearly every dimension of outputs, versus our own human intelligence [Chollet (2017)]. contemporary digital life. From medical diagnostics to recom- In grouping these methods under the moniker “artificial in- mendation systems—such as those employed by Amazon or telligence,” we risk imbuing them with faculties they do not Netflix—adaptive computer algorithms have radically altered have.
    [Show full text]
  • Everybody Is Talking About Virtual Assistants, but How Are Users Really Using Them?
    Everybody is talking about Virtual Assistants, but how are users really using them? 32nd Human Computer Interaction Conference, July 2018 Dr Marta Pérez García Sarita Saffon López Héctor Donis https://doi.org/10.14236/ewic/HCI2018.96 Everybody is talking about Virtual Assistants, but how are users really using them? The 2010s arrived focusing on algorithms of machine learning by enabling computers to have access to large Abstract amounts of data, which comes back to what was expected in the 1950s (Samuel, 1959; Koza, 1996). This Voice activated virtual assistants are growing rapidly in kind of application through a simplified interaction in number, variety and visibility, driven by media coverage, games and hobbies is what enabled the adoption of AI corporate communications, and inclusion in a growing at a user level. What is happening with AI variety of devices. This trend can also be observed by implementation today on our daily basis then? One of how difficult it is becoming to find, among internet many examples of our closest and most frequent users, people who have not used or even heard of this interactions with it is the virtual personal assistants new technology. Having said this, there is a visible (Arafa and Mamdani, 2000). shortage of academic research on this topic. Therefore, in the interest of creating a knowledge base around Regardless of the wave of technology adoption with voice activated virtual assistants based on artificial virtual assistants, little has been written in academia intelligence, this multi-country exploratory research about it so that theory can be built upon. Most study was carried out.
    [Show full text]
  • Logic and Ontology
    Logic and Ontology Nino B. Cocchiarella Abstract A brief review of the historical relation between logic and ontology and of the opposition between the views of logic as language and logic as calculus is given. We argue that predication is more fundamental than membership and that di¤erent theories of predication are based on di¤erent theories of universals, the three most important being nominalism, conceptualism, and realism. These theories can be formulated as formal ontologies, each with its own logic, and compared with one another in terms of their respective explanatory powers. After a brief survey of such a comparison, we argue that an extended form of conceptual realism provides the most coherent formal ontology and, as such, can be used to defend the view of logic as language. Logic, as Father Bochenski observed, developed originally out of dialectics, rules for discussion and reasoning, and in particular rules for how to argue suc- cessfully.1 Aristotle, one of the founders of logic in western philosophy, described such rules in his Topics, where logic is presented in this way. Indeed, the idea of logic as the art of arguing was the primary view of this subject in ancient philos- ophy. It was defended in particular by the Stoics, who developed a formal logic of propositions, but conceived of it only as a set of rules for arguing.2 Aristotle was the founder not only of logic in western philosophy, but of ontol- ogy as well, which he described in his Metaphysics and the Categories as a study of the common properties of all entities, and of the categorial aspects into which they can be analyzed.
    [Show full text]
  • A Leibnizian Approach to Mathematical Relationships: a New Look at Synthetic Judgments in Mathematics
    A Thesis entitled A Leibnizian Approach to Mathematical Relationships: A New Look at Synthetic Judgments in Mathematics by David T. Purser Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Arts Degree in Philosophy ____________________________________ Dr. Madeline M. Muntersbjorn, Committee Chair ____________________________________ Dr. John Sarnecki, Committee Member ____________________________________ Dr. Benjamin S. Pryor, Committee Member ____________________________________ Dr. Patricia Komuniecki, Dean College of Graduate Studies The University of Toledo December 2009 An Abstract of A Leibnizian Approach to Mathematical Relationships: A New Look at Synthetic Judgments in Mathematics by David T. Purser Submitted to the Graduate Faculty in partial fulfillment of the requirements for the Master of Arts Degree in Philosophy The University of Toledo May 2010 I examine the methods of Georg Cantor and Kurt Gödel in order to understand how new symbolic innovations aided in mathematical discoveries during the early 20th Century by looking at the distinction between the lingua characterstica and the calculus ratiocinator in the work of Leibniz. I explore the dynamics of innovative symbolic systems and how arbitrary systems of signification reveal real relationships in possible worlds. Examining the historical articulation of the analytic/synthetic distinction, I argue that mathematics is synthetic in nature. I formulate a moderate version of mathematical realism called modal relationalism. iii Contents Abstract iii Contents iv Preface vi 1 Leibniz and Symbolic Language 1 1.1 Introduction……………………………………………………. 1 1.2 Universal Characteristic……………………………………….. 4 1.2.1 Simple Concepts……………………………………….. 5 1.2.2 Arbitrary Signs………………………………………… 8 1.3 Logical Calculus………………………………………………. 11 1.4 Leibniz’s Legacy……………………………………………… 16 1.5 Leibniz’s Continued Relevance……………………………….
    [Show full text]
  • Data Models for Home Services
    __________________________________________PROCEEDING OF THE 13TH CONFERENCE OF FRUCT ASSOCIATION Data Models for Home Services Vadym Kramar, Markku Korhonen, Yury Sergeev Oulu University of Applied Sciences, School of Engineering Raahe, Finland {vadym.kramar, markku.korhonen, yury.sergeev}@oamk.fi Abstract An ultimate penetration of communication technologies allowing web access has enriched a conception of smart homes with new paradigms of home services. Modern home services range far beyond such notions as Home Automation or Use of Internet. The services expose their ubiquitous nature by being integrated into smart environments, and provisioned through a variety of end-user devices. Computational intelligence require a use of knowledge technologies, and within a given domain, such requirement as a compliance with modern web architecture is essential. This is where Semantic Web technologies excel. A given work presents an overview of important terms, vocabularies, and data models that may be utilised in data and knowledge engineering with respect to home services. Index Terms: Context, Data engineering, Data models, Knowledge engineering, Semantic Web, Smart homes, Ubiquitous computing. I. INTRODUCTION In recent years, a use of Semantic Web technologies to build a giant information space has shown certain benefits. Rapid development of Web 3.0 and a use of its principle in web applications is the best evidence of such benefits. A traditional database design in still and will be widely used in web applications. One of the most important reason for that is a vast number of databases developed over years and used in a variety of applications varying from simple web services to enterprise portals. In accordance to Forrester Research though a growing number of document, or knowledge bases, such as NoSQL is not a hype anymore [1].
    [Show full text]
  • Integrating Fuzzy Logic in Ontologies
    INTEGRATING FUZZY LOGIC IN ONTOLOGIES Silvia Calegari and Davide Ciucci Dipartimento di Informatica, Sistemistica e Comunicazione, Universita` degli Studi di Milano Bicocca, via Bicocca degli Arcimboldi 8, 20126 Milano, Italy Keywords: Concept modifiers, fuzzy logics, fuzzy ontologies, membership modifiers, KAON, ontology editor. Abstract: Ontologies have proved to be very useful in sharing concepts across applications in an unambiguous way. Nowadays, in ontology-based applications information is often vague and imprecise. This is a well-known problem especially for semantics-based applications, such as e-commerce, knowledge management, web por- tals, etc. In computer-aided reasoning, the predominant paradigm to manage vague knowledge is fuzzy set theory. This paper presents an enrichment of classical computational ontologies with fuzzy logic to create fuzzy ontologies. So, it is a step towards facing the nuances of natural languages with ontologies. Our pro- posal is developed in the KAON ontology editor, that allows to handle ontology concepts in an high-level environment. 1 INTRODUCTION A possible solution to handle uncertain data and, hence, to tackle these problems, is to incorporate fuzzy logic into ontologies. The aim of fuzzy set An ontology is a formal conceptualization of a partic- theory (Klir and Yuan, 1995) introduced by L. A. ular domain of interest shared among heterogeneous Zadeh (Zadeh, 1965) is to describe vague concepts applications. It consists of entities, attributes, rela- through a generalized notion of set, according to tionships and axioms to provide a common under- which an object may belong to a certain degree (typ- standing of the real world (Lammari and Mtais, 2004; ically a real number from the interval [0,1]) to a set.
    [Show full text]
  • The Role of Ontology in Information Management
    UNLV Retrospective Theses & Dissertations 1-1-2003 The role of ontology in information management Renato de Freitas Marteleto University of Nevada, Las Vegas Follow this and additional works at: https://digitalscholarship.unlv.edu/rtds Repository Citation Marteleto, Renato de Freitas, "The role of ontology in information management" (2003). UNLV Retrospective Theses & Dissertations. 1494. http://dx.doi.org/10.25669/0fz6-zsye This Thesis is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in UNLV Retrospective Theses & Dissertations by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. THE ROLE OF ONTOLOGY IN INFORMATION MANAGEMENT by Renato de Ereitas Marteleto Bachelor of Science Universidade Federal de Ouro Preto - Brazil 1999 A thesis submitted in partial fulfillment of the requirements for the Master of Science in Computer Science Department of Computer Science Howard R. Hughes College of Engineering Graduate College University of Nevada, Las Vegas M ay 2003 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UMI Number: 1414535 Marteleto, Renato de Freitas All rights reserved. UMI UMI Microform 1414535 Copyright 2003 by ProQuest Information and Learning Company.
    [Show full text]
  • Practical Reasoning in Probabilistic Description Logic
    PRACTICAL REASONING IN PROBABILISTIC DESCRIPTION LOGIC A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical Sciences 2011 By Pavel Klinov School of Computer Science 2 Contents Abstract 9 Declaration 10 Copyright 11 Acknowledgments 12 1 Introduction 13 1.1 Description Logic and Ontologies . 13 1.1.1 Description Logic: From Semantic Nets to OWL 2 . 14 1.1.2 Ontologies at Work . 15 1.2 Uncertainty . 16 1.2.1 Uncertainty: Ubiquitous and Versatile . 16 1.2.2 Uncertainty vs. Vagueness . 18 1.3 P-SROIQ and Pronto Reasoner . 18 1.4 Objectives . 20 1.5 Thesis Structure . 20 2 Background and Related Work 22 2.1 Description Logic . 22 2.1.1 Syntax and Semantics . 22 2.1.2 Reasoning Problems and Complexity . 25 2.1.3 The Web Ontology Language . 26 2.2 Representation and Reasoning About Uncertainty . 26 2.2.1 Knowledge Based Model Construction . 27 2.2.2 Probabilistic Logics . 34 2.3 P-SROIQ: A Probabilistic Description Logic . 48 2.3.1 Syntax and Semantics . 48 2.3.2 Reasoning Problems . 51 2.3.3 Original Algorithms . 53 3 2.3.4 Note on Probabilistic Coherence . 57 2.4 Related Work . 58 2.4.1 Propositional PSAT Solvers . 59 2.4.2 NMPROBLOG and ContraBovemRufum . 61 3 Applications and Case Studies 62 3.1 Breast Cancer Risk Assessment Problem . 62 3.1.1 Risk Types and Assessment . 63 3.1.2 Existing Tools and Models . 65 3.1.3 The BCRA Problem and P-SROIQ ...............
    [Show full text]