semantic web technologies trends and research in ontology-based systems

Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com Semantic Web Technologies Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com Semantic Web Technologies Trends and Research in Ontology-based Systems John Davies BT, UK Rudi Studer University of Karlsruhe, Germany Paul Warren BT, UK Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com Copyright # 2006 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, England Telephone (þ44) 1243 779777 Email (for orders and customer service enquiries): cs-books@wiley.co.uk Visit our Home Page on www.wiley.com All Rights Reserved. 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If professional advice or other expert assistance is required, the services of a competent professional should be sought. Other Wiley Editorial Offices John Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USA Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USA Wiley-VCH Verlag GmbH, Boschstr. 12, D-69469 Weinheim, Germany John Wiley & Sons Australia Ltd, 42 McDougall Street, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809 John Wiley & Sons Canada Ltd, 22 Worcester Road, Etobicoke, Ontario, Canada M9W 1L1 Library of Congress Cataloging-in-Publication Data Davies, J. (N. John) Semantic Web technologies : trends and research in ontology-based systems / John Davies, Rudi Studer, Paul Warren. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-470-02596-3 (cloth : alk. paper) ISBN-10: 0-470-02596-4 (cloth : alk. paper) 1. Semantic Web. I. Studer, Rudi. II. Warren, Paul. III. Title: Trends and research in ontology-based systems. IV. Title. TK5105.88815.D38 2006 025.04–dc22 2006006501 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN-13: 978-0-470-02596-3 ISBN-10: 0-470-02596-4 Typeset in 10/11.5 pt Palatino by Thomson Press (India) Ltd, New Delhi, India Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham, Wiltshire This book is printed on acid-free paper responsibly manufactured from sustainable forestry in which at least two trees are planted for each one used for paper production. Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com Contents Foreword xi 1. Introduction 1 1.1. Semantic Web Technologies 1 1.2. The Goal of the Semantic Web 2 1.3. Ontologies and Ontology Languages 4 1.4. Creating and Managing Ontologies 5 1.5. Using Ontologies 6 1.6. Applications 7 1.7. Developing the Semantic Web 8 References 8 2. Knowledge Discovery for Ontology Construction 9 2.1. Introduction 9 2.2. Knowledge Discovery 10 2.3. Ontology Definition 10 2.4. Methodology for Semi-automatic Ontology Construction 11 2.5. Ontology Learning Scenarios 12 2.6. Using Knowledge Discovery for Ontology Learning 13 2.6.1. Unsupervised Learning 14 2.6.2. Semi-Supervised, Supervised, and Active Learning 16 2.6.3. Stream Mining and Web Mining 18 2.6.4. Focused Crawling 18 2.6.5. Data Visualization 19 2.7. Related Work on Ontology Construction 22 2.8. Discussion and Conclusion 24 Acknowledgments 24 References 25 3. Semantic Annotation and Human Language Technology 29 3.1. Introduction 29 3.2. Information Extraction: A Brief Introduction 31 Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com 3.2.1. Five Types of IE 32 3.2.2. Entities 33 3.2.3. Mentions 33 3.2.4. Descriptions 34 3.2.5. Relations 34 3.2.6. Events 34 3.3. Semantic Annotation 35 3.3.1. What is Ontology-Based Information Extraction 36 3.4. Applying ‘Traditional’ IE in Semantic Web Applications 37 3.4.1. AeroDAML 38 3.4.2. Amilcare 38 3.4.3. MnM 39 3.4.4. S-Cream 39 3.4.5. Discussion 40 3.5. Ontology-based IE 40 3.5.1. Magpie 40 3.5.2. Pankow 41 3.5.3. SemTag 41 3.5.4. Kim 42 3.5.5. KIM Front-ends 43 3.6. Deterministic Ontology Authoring using Controlled Language IE 45 3.7. Conclusion 48 References 49 4. Ontology Evolution 51 4.1. Introduction 51 4.2. Ontology Evolution: State-of-the-art 52 4.2.1. Change Capturing 53 4.2.2. Change Representation 54 4.2.3. Semantics of Change 56 4.2.4. Change Propagation 58 4.2.5. Change Implementation 59 4.2.6. Change Validation 60 4.3. Logical Architecture 60 4.4. Data-driven Ontology Changes 62 4.4.1. Incremental Ontology Learning 64 4.5. Usage-driven Ontology Changes 66 4.5.1. Usage-driven Hierarchy Pruning 67 4.6. Conclusion 68 References 69 5. Reasoning With Inconsistent Ontologies: Framework, Prototype, and Experiment 71 5.1. Introduction 71 5.2. Brief Survey of Approaches to Reasoning with Inconsistency 73 5.2.1. Paraconsistent Logics 73 vi CONTENTS Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com 5.2.2. Ontology Diagnosis 74 5.2.3. Belief Revision 74 5.2.4. Synthesis 75 5.3. Brief Survey of Causes for Inconsistency in the Semantic Web 75 5.3.1. Inconsistency by Mis-representation of Default 75 5.3.2. Inconsistency Caused by Polysemy 77 5.3.3. Inconsistency through Migration from Another Formalism 77 5.3.4. Inconsistency Caused by Multiple Sources 78 5.4. Reasoning with Inconsistent Ontologies 79 5.4.1. Inconsistency Detection 79 5.4.2. Formal Definitions 80 5.5. Selection Functions 82 5.6. Strategies for Selection Functions 83 5.7. Syntactic Relevance-Based Selection Functions 85 5.8. Prototype of Pion 87 5.8.1. Implementation 87 5.8.2. Experiments and Evaluation 88 5.8.3. Future Experiments 91 5.9. Discussion and Conclusions 91 Acknowledgment 92 References 92 6. Ontology Mediation, Merging, and Aligning 95 6.1. Introduction 95 6.2. Approaches in Ontology Mediation 96 6.2.1. Ontology Mismatches 97 6.2.2. Ontology Mapping 97 6.2.3. Ontology Alignment 100 6.2.4. Ontology Merging 102 6.3. Mapping and Querying Disparate Knowledge Bases 104 6.3.1. Mapping Language 106 6.3.2. A (Semi-)Automatic Process for Ontology Alignment 108 6.3.3. OntoMap: an Ontology Mapping Tool 110 6.4. Summary 111 References 112 7. Ontologies for Knowledge Management 115 7.1. Introduction 115 7.2. Ontology Usage Scenario 116 7.3. Terminology 117 7.3.1. Data Qualia 119 7.3.2. Sorts of Data 120 7.4. Ontologies as RDBMS Schema 123 7.5. Topic-ontologies Versus Schema-ontologies 124 7.6. Proton Ontology 126 7.6.1. Design Rationales 126 CONTENTS vii Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com 7.6.2. Basic Structure 127 7.6.3. Scope, Coverage, Compliance 128 7.6.4. The Architecture of Proton 130 7.6.5. Topics in Proton 131 7.6.6. Proton Knowledge Management Module 133 7.7. Conclusion 135 References 136 8. Semantic Information Access 139 8.1. Introduction 139 8.2. Knowledge Access and the Semantic WEB 139 8.2.1. Limitations of Current Search Technology 140 8.2.2. Role of Semantic Technology 142 8.2.3. Searching XML 143 8.2.4. Searching RDF 144 8.2.5. Exploiting Domain-specific Knowledge 146 8.2.6. Searching for Semantic Web Resources 150 8.2.7. Semantic Browsing 151 8.3. Natural Language Generation from Ontologies 152 8.3.1. Generation from Taxonomies 153 8.3.2. Generation of Interactive Information Sheets 154 8.3.3. Ontology Verbalisers 154 8.3.4. Ontogeneration 154 8.3.5. Ontosum and Miakt Summary Generators 155 8.4. Device Independence: Information Anywhere 156 8.4.1. Issues in Device Independence 157 8.4.2. Device Independence Architectures and Technologies 160 8.4.3. DIWAF 162 8.5. SEKTAgent 164 8.6. Concluding Remarks 166 References 167 9. Ontology Engineering Methodologies 171 9.1. Introduction 171 9.2. The Methodology Focus 172 9.2.1. Definition of Methodology for Ontologies 172 9.2.2. Methodology 173 9.2.3. Documentation 174 9.2.4. Evaluation 174 9.3. Past and Current Research 174 9.3.1. Methodologies 174 9.3.2. Ontology Engineering Tools 177 9.3.3. Discussion and Open Issues 178 9.4. Diligent Methodology 180 9.4.1. Process 180 9.4.2. Argumentation Support 183 viii CONTENTS Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com 9.5. First Lessons Learned 185 9.6. Conclusion and Next Steps 186 References 187 10. Semantic Web Services – Approaches and Perspectives 191 10.1. Semantic Web Services – A Short Overview 191 10.2. The WSMO Approach 192 10.2.1. The Conceptual Model – The Web Services Modeling Ontology (WSMO) 193 10.2.2. The Language – The Web Service Modeling Language (WSML) 198 10.2.3. The Execution Environment – The Web Service Modeling Execution Environment (WSMX) 204 10.3. The OWL-S Approach 207 10.3.1. OWL-S Service Profiles 209 10.3.2. OWL-S Service Models 210 10.4. The SWSF Approach 213 10.4.1. The Semantic Web Services Ontology (SWSO) 213 10.4.2. The Semantic Web Services Language (SWSL) 216 10.5. The IRS-III Approach 218 10.5.1. Principles Underlying IRS-III 218 10.5.2. The IRS-III Architecture 220 10.5.3. Extension to WSMO 221 10.6. The WSDL-S Approach 222 10.6.1. Aims and Principles 222 10.6.2. Semantic Annotations 224 10.7. Semantic Web Services Grounding: The Link Between SWS and Existing Web Services Standards 226 10.7.1. General Grounding Uses and Issues 226 10.7.2. Data Grounding 228 10.7.3. Behavioural Grounding 230 10.8. Conclusions and Outlook 232 References 234 11. Applying Semantic Technology to a Digital Library 237 11.1. Introduction 237 11.2. Digital Libraries: The State-of-the-art 238 11.2.1. Working Libraries 238 11.2.2. Challenges 239 11.2.3. The Research Environment 241 11.3. A Case Study: The BT Digital Library 242 11.3.1. The Starting Point 242 11.3.2. Enhancing the Library with Semantic Technology 244 11.4. The Users’ View 248 11.5. Implementing Semantic Technology in a Digital Library 250 11.5.1. Ontology Engineering 250 CONTENTS ix Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com [...]... available Web- Mining, another sub-field of Knowledge Discovery, addresses Web data including three interleaved threads of research: Web content mining, Web structure mining, and Web usage mining As ontologies are used in different applications and by different users, we can make an analogy between usage of ontologies and usage of Web pages For instance, in Web usage mining (Chakrabarti, 2002), by analyzing... learning and discovery in unstructured and semi-structured domains such as text (Text Mining), web (Web Mining), graphs/networks (Link Analysis), learning models in relational/first-order form (Relational Data Mining), analyzing data streams (Stream Mining), etc In these we see a great potential for addressing the task of semi-automatic ontology construction Knowledge Discovery can be seen as a research. .. Merge and Split Unregistered WEB Simpo PDF DEVELOPING THE SEMANTIC Version - http://www.simpopdf.com This book aims to provide the reader with an overview of the current state of the art in Semantic Web technologies, and their application It is hoped that, armed with this understanding, readers will feel inspired to further develop semantic web technologies and to use semantic web applications, and indeed... GOAL OF THE SEMANTIC WEB The Semantic Web and Semantic Web technologies offer us a new approach to managing information and processes, the fundamental principle of which is the creation and use of semantic metadata 1 See: http:/ /www.w3.org/2001/sw/ THE GOAL OF THE SEMANTIC WEB 3 For information, metadata can exist at two levels On the one hand, they Simpo PDF Merge and Split for example a web page,... domains and sectors Chapter 11 describes the key role which Semantic Web technology is playing in enhancing the concept of a Digital Library Interoperability between digital libraries is seen as a ‘Grand Challenge’, and Semantic Web technology is key to achieving such interoperability At the same time, the technology offers new ways of classifying, finding and presenting knowledge, and also the interrelationships... suggesting concepts as subsets of documents and (ii) suggesting naming of the concepts Suggesting concepts based on the document collection is based on representing documents as wordvectors and applying Document clustering or Latent Semantic Indexing (LSI) As ontology learning scenario 4 (described in Section 2.5) is one USING KNOWLEDGE DISCOVERY FOR ONTOLOGY LEARNING 15 of the most important and demanding,... a Semantic Web Technologies: Trends and Research in Ontology-based Systems John Davies, Rudi Studer, Paul Warren # 2006 John Wiley & Sons, Ltd 2 INTRODUCTION common view of all the data and understand their relationships He Simpo PDF Merge and Split Unregisteredother hand, as being concerned Version - http://www.simpopdf.com describes application integration, on the with sharing ‘data, information and. .. example to search for web services and then to combine them in a useful way Semantic web services, described in Chapter 10, offer the possibility of automating web service discovery, composition and invocation This will have considerable impact in areas such as e-Commerce and Enterprise Application Integration, by 4 http://proton.semanticweb.org/ APPLICATIONS 7 enabling dynamic and scalable cooperation... ontologies giving different views on the same data, etc More formally, we define the ontology learning tasks in terms of mappings between ontology components, where some of the components are given and some are missing and we want to induce the missing ones Some typical scenarios in ontology learning are the following: USING KNOWLEDGE DISCOVERY FOR ONTOLOGY LEARNING 13 1 Inducing concepts/clustering of instances... Documents can also be related in ways other than common words (for instance, hyperlinks connecting Web documents) and these connections can be also used in document categorization (e.g., Craven and Slattery, 2001) 1 The notion of a topic ontology is explored in detail in Chapter 7 18 KNOWLEDGE DISCOVERY FOR ONTOLOGY CONSTRUCTION 2.6.3 Stream Mining and Web Mining Simpo PDF Merge and Split Unregistered Version . points out that information integration is needed to ‘reach a better understanding of the business through its data’, that is to achieve a Semantic Web Technologies: Trends and Research in Ontology-based. THE SEMANTIC WEB The Semantic Web and Semantic Web technologies offer us a new approach to managing information and processes, the fundamental principle of which is the creation and use of semantic. Aims and Principles 222 10.6.2. Semantic Annotations 224 10.7. Semantic Web Services Grounding: The Link Between SWS and Existing Web Services Standards 226 10.7.1. General Grounding Uses and