DOCSLIB.ORG

Ontological Engineering: What Are Ontologies and How Can We Build Them?

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Ontological Engineering: What Are Ontologies and How Can We Build Them? 44 Chapter III Ontological Engineering: What Are Ontologies and How Can We Build Them? Oscar Corcho University of Manchester, UK Mariano Fernández-López Universidad San Pablo CEU and Universidad Politécnica de Madrid, Spain Asunción Gómez-Pérez Universidad Politécnica de Madrid, Spain ABSTRACT Ontologies are formal, explicit specifications of shared conceptualizations. There is much literature on what they are, how they can be engineered and where they can be used inside applications. All these literature can be grouped under the term “Ontological Engineering,” which is defined as the set of ac- tivities that concern the ontology development process, the ontology lifecycle, the principles, methods and methodologies for building ontologies, and the tool suites and languages that support them. In this chapter we provide an overview of Ontological Engineering, describing the current trends, issues and problems. INTRODUCTION the knowledge bases upon which they are based did not have to be constructed from scratch, but The origin of ontologies in computer science by assembling reusable components. This reuse can be referred back to 1991, in the context of applies both to static knowledge, which is modeled the DARPA Knowledge Sharing Effort (Neches, by means of ontologies, and dynamic problem- Fikes, Finin, Gruber, Senator, & Swartout, 1991). solving knowledge, which is modeled by means The aim of this project was to devise new ways of of problem solving methods. constructing knowledge-based systems, so that Copyright © 2007, Idea Group Inc., distributing in print or electronic forms without written permission of IGI is prohibited. Ontological Engineering Since then, considerable progress has been WHAT IS AN ONTOLOGY AND made in this area. Ontologies are now considered WHICH ARE ITS COMPONENTS? as a commodity that can be used for the develop- ment of a large number of applications in different There are two different views about the use of the fields, such as knowledge management, natural term “ontology,” considering whether the person language processing, e-commerce, intelligent who uses that term is interested in its philosophical integration information, information retrieval, roots or in its application to Computer Science. database design and integration, bio-informatics, For philosophers, the term Ontology (normally education, and so forth. typed with uppercase) refers to the “the essence The emergence of the Semantic Web (Bern- of things through the changes.” Greek philoso- ers-Lee, 1999) has caused a growing need for phers, from Parmenides of Elea to Aristotle, were knowledge reuse, and has strenghtened its poten- interested in these aspects. In the 18th century, tial at the same time. Therefore, ontologies and Kant worked also on these ideas. More recently, problem-solving methods (which in some cases people working in the area of formal ontologies are considered as the precursors of Semantic are also interested in these philosophical ideas Web Services) are playing an important role in and its application in the context of Computer this context. Science. As described in the chapter title, we will pres- On the other side, ontology engineers in the ent the what and how of ontologies, describing context of computer science are more interested the activities that should be carried out during the in how ontologies (typed with lowercase) can be ontology development process, the principles to used to represent reusable and sharable pieces of be followed in ontology design, and the methods, domain knowledge and how they can be used in ap- methodologies, software tools and languages that plications. In this context, ontologies are reusable give support to each one of these activities. The and sharable artifacts that have to be developed second section defines the word “ontology” and in a machine interpretable language ��������(Gruber, explains which are the main components that can 1993; Studer, ��������������������������������Benjamins, & Fensel�������������, 1998)������. This be used to model ontologies. The third section point of view is clearly addressed in the defini- focuses on methods and methodologies for the tion given by Studer and colleagues (1998): An development of ontologies, either used for the ontology is a formal, explicit specification of a whole ontology development process or only for shared conceptualization. We consider that this specific activities. The fourth section focuses on definition is one of the most complete ones from ontology tools, which normally give support to those available in the literature. the previous methodological approaches. The Once we have analysed these different defini- fifth section describes ontology languages that tions of the term “ontology,” we will focus on the can be used to implement ontologies. All these second use of this term, that is, on what is normally sections are structured in a similar way: first we known as Ontological Engineering (Gómez-Pérez, give a brief overview of their evolution, then we Fernández-López, & Corcho����������������������, 2003). First we will describe the current trends, and finally we pay discuss about the components that are used to attention to the open issues and practical aspects. create an ontology. Finally, conclusions and future lines of work are Different knowledge representation formal- presented in the last section. isms (and corresponding languages) exist for the 45 Ontological Engineering fomalisation (and implementation) of ontologies. According to Gruber (1993), formal axioms Each of them provides different components that serve to model sentences that are always true. can be used for these tasks. However, they share They are normally used to represent knowledge the following minimal set of components.1 that cannot be formally defined by the other Classes represent concepts, which are taken components. In addition, formal axioms are used in a broad sense. For instance, in the traveling to verify the consistency of the ontology itself domain, concepts are: locations (cities, vil- or the consistency of the knowledge stored in a lages, etc.), lodgings (hotels, camping, etc.) and knowledge base. Formal axioms are very useful means of transport (planes, trains, cars, ferries, to infer new knowledge. An axiom in the traveling motorbikes and ships). Classes in the ontology domain would be that it is not possible to travel are usually organised in taxonomies through from the America to Europe by train. which inheritance mechanisms can be applied. Instances are used to represent elements or We can represent a taxonomy of entertainment individuals in an ontology. An example of instance places (theater, cinema, concert, etc.) or travel of the concept AA7462 is the flight AA7462 that packages (economy travel, business travel, etc.). arrives at Seattle on February 8, 2006 and costs In the frame-based KR paradigm, metaclasses 300 (US Dollars, Euros, or any other currency). can also be defined. Metaclasses are classes Besides formalisms and languages specifically whose instances are classes. They usually allow designed for representing knowledge, ontologies for gradations of meaning, since they establish can be formalised with other approaches coming different layers of classes in the ontology where from the areas of Software Engineering, such as they are defined. the Unified Modeling Language (UML) (Rum- Relations represent a type of association be- baugh, Jacobson, & Booch, 1998) or Entity-Re- tween concepts of the domain. They are formally lationship (ER) Diagrams (Chen, 1976). defined as any subset of a product of n sets, that In this context, the Object Management Group is: R ⊂ C1 x C2 x ... x Cn. Ontologies usually (OMG)2 is working on a specification to define the contain binary relations. The first argument is meta-models of some of the diagram types and known as the domain of the relation, and the languages used in ontology representation. This second argument is the range. For instance, the specification is known as ontology description binary relation arrivalPlace has the concept Travel model (ODM, 2005), and uses a common formal as its domain and the concept Location as its range. notation to describe the metamodels. Such meta- Relations can be instantiated with knowledge from models (defined for UML, Entity-Relationship, the domain. For example, to express that the flight OWL, RDF(S), etc.) can be considered formalisa- AA7462-Feb-08-2002 arrives in Seattle we must tions of knowledge representation ontologies. All write: (arrivalPlace AA7462-Feb-08-2002 Seattle) these correspondences are formally described in Binary relations are sometimes used to ex- the ODM document (ODM, 2005). press concept attributes (i.e., slots). Attributes The purpose of ODM documents is to allow are usually distinguished from relations because software engineers to model ontologies with their range is a datatype, such as string, number, familiar notations for them, for example, UML and so forth, while the range of relations is a and ER, and to transform their conceptual models concept. The following code defines the attribute into formal ontologies represented in ontology flightNumber, which is a string. We can also express languages. relations of higher arity, such as “a road connects two different cities.” 46 Ontological Engineering METHODS AND METHODOLOGIES 1994, 1996, 2001, 2004; Guarino, 2004; Gua- FOR THE DEVELOPMENT OF rino & Welty, 2002; Kalfoglou & Robertson, ONTOLOGIES 1999a, 1999b; Welty & Guarino, 2001), ontology evolution (Klein & Fensel,
Load more

Recommended publications
  • 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]
  • Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs
    Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2017 Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs Vinh Thi Kim Nguyen Wright State University Follow this and additional works at: https://corescholar.libraries.wright.edu/etd_all Part of the Computer Engineering Commons, and the Computer Sciences Commons Repository Citation Nguyen, Vinh Thi Kim, "Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs" (2017). Browse all Theses and Dissertations. 1912. https://corescholar.libraries.wright.edu/etd_all/1912 This Dissertation is brought to you for free and open access by the Theses and Dissertations at CORE Scholar. It has been accepted for inclusion in Browse all Theses and Dissertations by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs A dissertation submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy By VINH THI KIM NGUYEN B.E., Vietnam National University, 2007 2017 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL December 15, 2017 I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPERVISION BY Vinh Thi Kim Nguyen ENTITLED Semantic Web Foundations of Representing, Reasoning, and Traversing in Contextualized Knowledge Graphs BE ACCEPTED IN PARTIAL FUL- FILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy. Amit Sheth, Ph.D. Dissertation Director Michael Raymer, Ph.D. Director, Computer Science and Engineering Ph.D. Program Barry Milligan, Ph.D. Interim Dean of the Graduate School Committee on Final Examination Amit Sheth, Ph.D.
    [Show full text]
  • Application of Ontological Models for Representing Engineering Concepts in Engineering
    SCIENTIFIC PROCEEDINGS X INTERNATIONAL CONGRESS "MACHINES, TECHNOLОGIES, MATERIALS" 2013 ISSN 1310-3946 APPLICATION OF ONTOLOGICAL MODELS FOR REPRESENTING ENGINEERING CONCEPTS IN ENGINEERING Martin Ivanov1, Nikolay Tontchev2 New Bulgarian University, Sofia, Montivideo 211, University of Transport, Sofia, Geo Milev 1582 Abstract: The necessity, opportunities and advantages from developing ontological models of engineering concepts in the field of machine manufacturing technology are introduced. The characteristics and principles of the methodology for developing engineering technology models are presented. An experimental ontological model developed by the authors based on Protégé OWL 4.2 is described. Keywords: ONTOLOGICAL MODELS, ENGINEERING CONCEPTS, IDEF5, PROTEGE OWL 1. Introduction - Existing objects and phenomena in the subject area in the terms of the accepted terminology (vocabulary/vocabularies, Modern engineering activities take place in an evolving and in this case – the so called engineering lexicon or EL); enriching information environment characterized by exponentially - How objects and phenomena relate to each other; increasing information flows, variety of means for access and in the formats of data presentation, diversity of engineering problems, - How they are used within the subject area, and outside it; solved by automated means, penetration of the artificial intelligence methods and the semantic Web, too. Effectiveness of engineering is - Rules that define their existence and behavior. highly dependent on the access and the opportunities for efficient The ontology is composed on the basis of a specific vocabulary processing of this information, in particular by the use of models for and terminology characteristic for the subject area. This implies (a) formally describing, processing and dissemination of knowledge strict definitions (axioms) of the terms and rules (grammars) resulting from research and engineering practice.
    [Show full text]
  • AI/ML Finding a Doing Machine
    Digital Readiness: AI/ML Finding a doing machine Gregg Vesonder Stevens Institute of Technology http://vesonder.com Three Talks • Digital Readiness: AI/ML, The thinking system quest. – Artificial Intelligence and Machine Learning (AI/ML) have had a fascinating evolution from 1950 to the present. This talk sketches the main themes of AI and machine learning, tracing the evolution of the field since its beginning in the 1950s and explaining some of its main concepts. These eras are characterized as “from knowledge is power” to “data is king”. • Digital Readiness: AI/ML, Finding a doing machine. – In the last decade Machine Learning had a remarkable success record. We will review reasons for that success, review the technology, examine areas of need and explore what happened to the rest of AI, GOFAI (Good Old Fashion AI). • Digital Readiness: AI/ML, Common Sense prevails? – Will there be another AI Winter? We will explore some clues to where the current AI/ML may reunite with GOFAI (Good Old Fashioned AI) and hopefully expand the utility of both. This will include extrapolating on the necessary melding of AI with engineering, particularly systems engineering. Roadmap • Systems – Watson – CYC – NELL – Alexa, Siri, Google Home • Technologies – Semantic web – GPUs and CUDA – Back office (Hadoop) – ML Bias • Last week’s questions Winter is Coming? • First Summer: Irrational Exuberance (1948 – 1966) • First Winter (1967 – 1977) • Second Summer: Knowledge is Power (1978 – 1987) • Second Winter (1988 – 2011) • Third Summer (2012 – ?) • Why there might not be a third winter! Henry Kautz – Engelmore Lecture SYSTEMS Winter 2 Systems • Knowledge is power theme • Influence of the web, try to represent all knowledge – Creating a general ontology organizing everything in the world into a hierarchy of categories – Successful deep ontologies: Gene Ontology and CML Chemical Markup Language • Indeed extreme knowledge – CYC and Open CYC – IBM’s Watson Upper ontology of the world Russell and Norvig figure 12.1 Properties of a subject area and how they are related Ferrucci, D., et.al.
    [Show full text]
  • Ontology and Information Systems
    Ontology and Information Systems 1 Barry Smith Philosophical Ontology Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ is often used by philosophers as a synonym for ‘metaphysics’ (literally: ‘what comes after the Physics’), a term which was used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’.2 The term ‘ontology’ (or ontologia) was itself coined in 1613, independently, by two philosophers, Rudolf Göckel (Goclenius), in his Lexicon philosophicum and Jacob Lorhard (Lorhardus), in his Theatrum philosophicum. The first occurrence in English recorded by the OED appears in Bailey’s dictionary of 1721, which defines ontology as ‘an Account of being in the Abstract’. Methods and Goals of Philosophical Ontology The methods of philosophical ontology are the methods of philosophy in general. They include the development of theories of wider or narrower scope and the testing and refinement of such theories by measuring them up, either against difficult 1 This paper is based upon work supported by the National Science Foundation under Grant No. BCS-9975557 (“Ontology and Geographic Categories”) and by the Alexander von Humboldt Foundation under the auspices of its Wolfgang Paul Program. Thanks go to Thomas Bittner, Olivier Bodenreider, Anita Burgun, Charles Dement, Andrew Frank, Angelika Franzke, Wolfgang Grassl, Pierre Grenon, Nicola Guarino, Patrick Hayes, Kathleen Hornsby, Ingvar Johansson, Fritz Lehmann, Chris Menzel, Kevin Mulligan, Chris Partridge, David W. Smith, William Rapaport, Daniel von Wachter, Chris Welty and Graham White for helpful comments.
    [Show full text]
  • Ontology Languages – a Review
    International Journal of Computer Theory and Engineering, Vol.2, No.6, December, 2010 1793-8201 Ontology Languages – A Review V. Maniraj, Dr.R. Sivakumar 1) Logical Languages Abstract—Ontologies have been becoming a hot research • First order predicate logic topic for the application in artificial intelligence, semantic web, Software Engineering, Library Science and information • Rule based logic Architecture. Ontology is a formal representation of set of concepts within a domain and relationships between those • concepts. It is used to reason about the properties of that Description logic domain and may be used to define the domain. An ontology language is a formal language used to encode the ontologies. A 2) Frame based Languages number of research languages have been designed and released • Similar to relational databases during the past few years by the research community. They are both proprietary and standard based. In this paper a study has 3) Graph based Languages been reported on the different features and issues of these • languages. This paper also addresses the challenges for Semantic network research community in the further development of ontology languages. • Analogy with the web is rationale for the semantic web I. INTRODUCTION Ontology engineering (or ontology building) is a subfield II. BACKGROUND of knowledge engineering that studies the methods and CycL1 in computer science and artificial intelligence is an methodologies for building ontologies. It studies the ontology language used by Doug Lenat’s Cye artificial ontology development process, the ontology life cycle, the intelligence project. Ramanathan V. Guna was instrumental methods and methodologies for building ontologies, and the in the design of the language.
    [Show full text]
  • Which Semantics?
    This item is the archived peer-reviewed author-version of: Tracing the origins of the semantic web Reference: Guns Raf.- Tracing the origins of the semantic web Journal of the American Society for Information Science and Technology / American Society for Information Science and Technology - ISSN 1532-2882 - 64:10(2013), p. 2173-2181 DOI: http://dx.doi.org/doi:10.1002/asi.22907 Handle: http://hdl.handle.net/10067/1111170151162165141 Institutional repository IRUA This paper is a postprint of the following article: Guns, R. (2013). Tracing the origins of the Semantic Web. Journal of the American Society for Information Science and Technology, 64(10), 2173–2181. http://dx.doi.org/10.1002/asi.22907 Tracing the origins of the Semantic Web Raf Guns Universiteit Antwerpen, IBW, Venusstraat 35, B-2000 Antwerp, Belgium Abstract The Semantic Web has been criticized for not being semantic. This article examines the question: why and how has the Web of Data, expressed in RDF, come to be known as the Semantic Web? Contrary to previous papers, we deliberately take a descriptive stance and do not start from preconceived ideas about the nature of semantics. Instead, we mainly base our analysis on early design documents of the (Semantic) Web. The main determining factor is shown to be link typing, coupled with the influence of online metadata. Both factors are already present in early Web standards and drafts. Our findings indicate that the Semantic Web is directly linked to older AI work, despite occasional claims to the contrary. Because of link typing, the Semantic Web can be considered an example of a semantic network.
    [Show full text]
  • Relating Ontology Languages and Web Standards
    Ontology Languages and Web Standards Relating Ontology Languages and Web Standards Dieter Fensel Abstract Currently computers are changing from single isolated devices to entry points in a world wide network of information exchange and business transactions called the World Wide Web (WWW). Therefore support in data, information, and knowledge exchange becomes the key issue in current computer technology. Ontologies provide a shared and common understanding of a domain that can be communicated between people and application systems. Therefore, they may play a major role in supporting information exchange processes in various areas. However, in order to develope their full power, the representation languages for ontologies must be compartible with existing data exchange standards in the World Wide Web. In this paper we investigate protoypical examples of ontology languages and web languages. Finally we show how these web standards can be used as a representation languages for ontologies. 1 Introduction Ontologies are a popular research topic in various communities such as knowledge engineering, natural language processing, cooperative information systems, intelligent information integration, and knowledge management. They provide a shared and common understanding of a domain that can be communicated between people and heterogeneous and distributed application systems. They have been developed in Artificial Intelligence to facilitate knowledge sharing and reuse. Ontologies are formal theories about a certain domain of discourse and therefore require a formal logical language to express them. The paper will discuss languages for describing ontologies in Section 2. In Section 3 we will investigate how recent web standards such as XML and RDF can be used as languages for expressing Ontologies, or at least some of their aspects.
    [Show full text]
  • CS 343 Artificial Intelligence
    CS 343 Artificial Intelligence Gordon S. Novak Jr. Department of Computer Sciences University of Texas at Austin Austin, Texas 78712 (512) 471-9569 [email protected] http://www.cs.utexas.edu/users/novak Copyright c Gordon S. Novak Jr. 1 Artificial Intelligence as Science Intelligence should be placed in the context of biology: Intelligence connects perception to action to help an organism survive. Intelligence is computation in the service of life, just as metabolism is chemistry in the service of life. Intelligence does not imply perfect understanding; every intelligent being has limited perception, memory, and computation. Many points on the spectrum of intelligence-versus-cost are viable, from insects to humans. AI seeks to understand the computations required for intelligent behavior and to produce computer systems that exhibit intelligence. Aspects of intelligence studied by AI include perception, motor control, communication using human languages, reasoning, planning, learning, and memory. 2 Scientific Goals of AI AI seeks to understand the working of the mind in mechanistic terms, just as medicine seeks to understand the working of the body in mechanistic terms. The mind is what the brain does. { Marvin Minsky The strong AI position is that any aspect of human intelligence could, in principle, be mechanized. 3 A.I. as Engineering How can we make computer systems more intelligent? • Perception to get input directly from the real world. • Autonomy to perform tasks that currently require human operators without human intervention or monitoring. • Flexibility in dealing with variability in the environment. • Ease of use: computers that are able to understand what the user wants from limited instructions in natural languages.
    [Show full text]
  • Glen Hart • Catherine Dolbear
    Glen Hart • Catherine Dolbear Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2013 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed in the United States of America on acid-free paper Version Date: 2012904 International Standard Book Number: 978-1-4398-6995-6 (Hardback) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data Hart, Glen, 1959- Linked data : A Geographic Perspective / Glen Hart and Catherine Dolbear. p.
    [Show full text]
  • A Layered Model for Building Ontology Translation Systems
    A layered model for building ontology translation systems Oscar Corcho, Asunción Gómez-Pérez University of Manchester. Kilburn Building, Room 1.17. Oxford Road, Manchester. M13 9PL. United Kingdom Ontological Engineering Group. Universidad Politécnica de Madrid. 28660 Boadilla del Monte (Madrid), Spain e-mail: [email protected], [email protected] Editor’s notes To be added Abstract. We present a model for building ontology translation systems between ontology languages and/or ontology tools, where translation decisions are defined at four different layers: lexical, syntax, semantic, and pragmatic. This layered approach makes ontology translation systems easier to build and understand, and consequently, to maintain and reuse. As part of this model, we propose a method that guides in the process of developing ontology translation systems according to this approach. The method identifies four main activities: feasibility study, analysis of source and target formats, design, and implementation of the translation system, with their decomposition in tasks, and recommends the techniques to be used inside each of them. 1. Introduction An ontology is defined as a “formal explicit specification of a shared conceptualisation” [Studer et al., 1998], that is, an ontology must be machine readable (it is formal), all its components must be described clearly (it is explicit), it describes an abstract model of a domain (it is a conceptualisation) and it is the product of a consensus (it is shared). Ontologies can be implemented in varied ontology languages, which are usually divided in two groups: classical and ontology markup languages. Among the classical languages used for ontology construction we can cite (in alphabetical order): CycL [Lenat and Guha, 1990], FLogic [Kifer et al, 1995], KIF [Genesereth and Fikes, 1992], LOOM [MacGregor, 1991], OCML [Motta, 1999], and Ontolingua [Gruber, 1992].
    [Show full text]
  • Creating Ontology-Based Metadata by Annotation for the Semantic Web
    Zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften (Dr. rer. pol.) von der Fakult¨at f¨ur Wirtschaftswissenschaften der Universit¨at Fridericiana zu Karlsruhe genehmigte Dissertation. Creating Ontology-based Metadata by Annotation for the Semantic Web von Dipl.-Ing.(FH), Dipl.-Inf.wiss. Siegfried Handschuh Tag der m¨undlichen Pr¨ufung: 16. Februar 2005 Referent: Prof. Dr. Rudi Studer Korreferent: Prof. Dr. Christof Weinhardt To my family. Acknowledgements I would like to thank people that supported me during the process of researching and writing this thesis. First of all, I would like to express my gratitude to my advisor, Prof. Dr. Rudi Studer, for his support, patience, and encouragement throughout my PhD studies at the Institute AIFB at the University of Karlsruhe. I am also grateful to Prof. Dr. Christof Weinhardt on my dissertation committee, and to Prof. Dr. Andreas Oberweis, who served on the examination committee. My work very much profited from working with Prof. Dr. Steffen Staab. I learned a lot from him, he inspiried me with his ideas and he significantly improved my scientific skills, e.g. by teaching me how to successfully write papers. He and Prof. Dr. Gerd Stumme gave me invaluable advice on organizing research workshops. My thanks also goes to all my colleagues in Karlsruhe at the Institute AIFB, the FZI and Ontoprise GmbH for providing a very fruitful and stimulating working atmosphere. In particular to Philipp Cimiano, Nenad and Ljiljana Stojanovic, Sudhir Agarwal and Alexander M¨adche. I’m especially indebted to Dr. Stefan Decker, for taking me on board his Onto- Agents project and the DARPA DAML program, which funded my work on semantic annotation.
    [Show full text]