Stuckenschmidt / Harmelen Information Sharing on the Semantic Web

1. Auflage 2005
ISBN: 978-3-540-26907-6
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 278 Seiten

Reihe: Advanced Information and Knowledge Processing

Details recent research in areas such as ontology design for information integration, metadata generation and management, and representation and management of distributed ontologies. Provides decision support on the use of novel technologies, information about potential problems, and guidelines for the successful application of existing technologies.

  Heiner Stuckenschmidt holds a post-doc position in the Knowledge Representation and Reasoning Group at the Vrije Universiteit Amsterdam where he received his PhD for work on Ontology-Based Information Sharing on the Semantic Web. His works include Semantic Web related topics such as ontology languages, knowledge-based meta data management and robust and scalable terminological reasoning. Before moving to Amsterdam, he was employed as a researcher and lecturer at the University of Bremen. His research activities in Bremen included the application of ontologies for information sharing in web based information systems with a special focus on semantics-preserving information integration and spatially related information. He is organizer of a series of workshops on ontologies at international conferences (IJCAI'01, ECAI'02, IJCAI'03) and member of program committees of several semantic web related conferences (SWWS'01, ISWC'02, WWW'03) and has held / will hold tutorial on the topic of the proposed book at different conferences (K-CAP 2001, IJCAI'03, ISWC'03). He is editor of a book on the application of ontologies in the cadastral domain and has published more than 40 paper in international journals, conferences and workshops.   Frank van Harmelen is professor in Knowledge Representation and Reasoning at the Department of Artificial Intelligence of the Vrije Universiteit Amsterdam. He was awarded a PhD from the Department of AI in Edinburgh for his research on meta-level reasoning, after having studied Mathematics and Computer Science in Amsterdam. He is author of a book on meta-level inference, editor of a book on knowledge-based systems, editor of a book on Knowledge Management on the Semantic Web, and is currently preparing a text-book on Semantic Web languages. He has published over 100 papers, many of them in leading journals and conferences. He has made key contributions to the CommonKADS project by providing a formal basis for the conceptual models. More recently, he has been co-projectmanager of the On-To-Knowledge project, and was one of the designers of OIL, which (in its form DAML +OIL) is currently the basis for a W3C standardized Web ontology language. He is a member of the joint EU/US committee on agent markup languages (who designed DAML+OIL), and is an active member of the W3C working group on Web Ontology languages, responsible for the OWL Web Ontology Language. He was the 2002 Program Chair of the European Conference on Artificial Intelligence, and will be the General Chair of the 2004 International Semantic Web Conference.

Autoren/Hrsg.

Stuckenschmidt, Heiner

Harmelen, Frank Van

Inhaltsverzeichnis

1;Preface;6
1.1;About the book;6
1.1.1;The success of the information society;6
1.1.2;The remaining problems;7
1.1.3;Intended readership;9
1.2;Organization of the Book;9
1.3;Acknowledgements;11
2;Contents;13
3;Part I Information sharing and ontologies;18
3.1;1 Semantic integration;19
3.1.1;1.1 Syntactic standards;20
3.1.1.1;1.1.1 HTML: visualizing information;20
3.1.1.2;1.1.2 XML: exchanging information;21
3.1.1.3;1.1.3 RDF: a data model for meta-information;22
3.1.1.4;1.1.4 The roles of XML and RDF;24
3.1.2;1.2 The Problem of Heterogeneity;26
3.1.2.1;1.2.1 Structural Conflicts;26
3.1.2.2;1.2.2 Semantic Conflicts;28
3.1.3;1.3 Handling information semantics;30
3.1.3.1;1.3.1 Semantics from structure;31
3.1.3.2;1.3.2 Semantics from text;32
3.1.3.3;1.3.3 The need for explicit semantics;33
3.1.4;1.4 Representing and comparing semantics;35
3.1.4.1;1.4.1 Names and labels;36
3.1.4.2;1.4.2 Term networks;36
3.1.4.3;1.4.3 Concept lattices;37
3.1.4.4;1.4.4 Features and constraints;38
3.1.5;1.5 Conclusion;39
3.1.5.1;Further Reading;39
3.2;2 Ontology-based information sharing;40
3.2.1;2.1 Ontologies;40
3.2.1.1;2.1.1 Shared vocabularies and conceptualizations;41
3.2.1.2;2.1.2 Speci.cation of context knowledge;42
3.2.1.3;2.1.3 Beneficial applications;44
3.2.2;2.2 Ontologies in information integration;46
3.2.2.1;2.2.1 Content explication;46
3.2.2.2;2.2.2 Additional roles of ontologies;49
3.2.3;2.3 A framework for information sharing;51
3.2.4;2.4 A translation approach to ontology alignment;53
3.2.4.1;2.4.1 The translation process;54
3.2.4.2;2.4.2 Required infrastructure;55
3.2.5;2.5 Conclusions;57
3.3;3 Ontology languages for the Semantic Web;60
3.3.1;3.1 An abstract view;60
3.3.2;3.2 Two Semantic Web ontology languages;62
3.3.2.1;3.2.1 RDF Schema;64
3.3.2.2;3.2.2 OWL Lite;65
3.3.2.3;3.2.3 OWL DL;67
3.3.2.4;3.2.4 OWL Full;68
3.3.2.5;3.2.5 Computational Complexity;69
3.3.2.6;3.2.6 Simple relations between ontologies;69
3.3.3;3.3 Other Web-based ontology languages;73
3.3.3.1;3.3.1 Languages for expressing ontology mappings;75
3.3.4;3.4 Conclusions;76
4;Part II Creating ontologies and metadata;77
4.1;4 Ontology creation;78
4.1.1;4.1 Ontological engineering;79
4.1.2;4.2 Building an ontology infrastructure for Information sharing;81
4.1.3;4.3 Applying the approach;83
4.1.3.1;4.3.1 The task to be solved;84
4.1.3.2;4.3.2 The Information Sources;85
4.1.3.3;4.3.3 Sources of knowledge;86
4.1.4;4.4 An example walkthrough;89
4.1.5;4.5 Conclusions;95
4.2;5 Metadata generation;97
4.2.1;5.1 The role of metadata;98
4.2.1.1;5.1.1 Use of metadata;99
4.2.1.2;5.1.2 Problems with metadata management;100
4.2.2;5.2 The WebMaster approach;102
4.2.2.1;5.2.1 BUISY: A Web based environmental information system;102
4.2.2.2;5.2.2 The WebMaster Workbench;103
4.2.2.3;5.2.3 Applying WebMaster to the BUISY system;105
4.2.3;5.3 Learning classification rules;109
4.2.3.1;5.3.1 Inductive logic programming;110
4.2.3.2;5.3.2 Applying inductive logic programming;112
4.2.3.3;5.3.3 Learning experiments;114
4.2.3.4;5.3.4 Extracted classi.cation rules;118
4.2.4;5.4 Ontology deployment;122
4.2.4.1;5.4.1 Generating ontology-based metadata;123
4.2.4.2;5.4.2 Using ontology-based metadata;124
4.2.5;5.5 Conclusions;126
5;Part III Retrieval, integration and querying;128
5.1;6 Retrieval and Integration;129
5.1.1;6.1 Semantic integration;130
5.1.1.1;6.1.1 Ontology heterogeneity;130
5.1.1.2;6.1.2 Multiple systems and translatability;132
5.1.1.3;6.1.3 Approximate re-classification;133
5.1.2;6.2 Concept-based filtering;135
5.1.2.1;6.2.1 The idea of query-rewriting;136
5.1.2.2;6.2.2 Boolean concept expressions;137
5.1.2.3;6.2.3 Query re-writing;139
5.1.3;6.3 Processing complex queries;141
5.1.3.1;6.3.1 Queries as concepts;142
5.1.3.2;6.3.2 Query relaxation;144
5.1.4;6.4 Examples from a case study;147
5.1.4.1;6.4.1 Concept approximations;147
5.1.4.2;6.4.2 Query relaxation;148
5.1.5;6.5 Conclusions;150
5.2;7 Sharing statistical information;152
5.2.1;7.1 The nature of statistical information;153
5.2.1.1;7.1.1 Statistical metadata;154
5.2.1.2;7.1.2 A basic ontology of statistics;155
5.2.2;7.2 Modelling Statistics;159
5.2.2.1;7.2.1 Statistics as views;159
5.2.2.2;7.2.2 Connection with the domain;160
5.2.3;7.3 Translation to Semantic Web languages;164
5.2.3.1;7.3.1 Ontologies;164
5.2.3.2;7.3.2 Description of information;168
5.2.4;7.4 Retrieving statistical information;171
5.2.5;7.5 Conclusions;173
5.3;8 Spatially-related information;175
5.3.1;8.1 Spatial representation and reasoning;176
5.3.1.1;8.1.1 Levels of spatial abstraction;176
5.3.1.2;8.1.2 Reasoning about spatial relations;177
5.3.2;8.2 Ontologies and spatial relevance;178
5.3.2.1;8.2.1 Defining Spatial Relevance;179
5.3.2.2;8.2.2 Combined spatial and terminological matching;180
5.3.2.3;8.2.3 Limitations;182
5.3.3;8.3 Graph-based reasoning about spatial relevance;183
5.3.3.1;8.3.1 Partonomies;184
5.3.3.2;8.3.2 Topology;186
5.3.3.3;8.3.3 Directions;187
5.3.3.4;8.3.4 Distances;188
5.3.4;8.4 Conclusions;190
5.4;9 Integration and retrieval systems;192
5.4.1;9.1 OntoBroker;193
5.4.1.1;9.1.1 F-Logic and its relation to OWL;194
5.4.1.2;9.1.2 Ontologies, sources and queries;196
5.4.1.3;9.1.3 Context transformation;198
5.4.2;9.2 OBSERVER;199
5.4.2.1;9.2.1 Query Processing in OBSERVER;200
5.4.2.2;9.2.2 Vocabulary integration;202
5.4.2.3;9.2.3 Query plan generation and selection;204
5.4.3;9.3 The BUSTER system;205
5.4.3.1;9.3.1 The use of shared vocabularies;207
5.4.3.2;9.3.2 Retrieving accommodation information;208
5.4.3.3;9.3.3 Spatial and temporal information;210
5.4.4;9.4 Conclusions;214
6;Part IV Distributed ontologies;215
6.1;10 Modularization;216
6.1.1;10.1 Motivation;217
6.1.1.1;10.1.1 Requirements;218
6.1.1.2;10.1.2 Our approach;218
6.1.1.3;10.1.3 Related work;219
6.1.2;10.2 Modular ontologies;221
6.1.2.1;10.2.1 Syntax and architecture;221
6.1.2.2;10.2.2 Semantics and logical consequence;222
6.1.3;10.3 Comparison with OWL;225
6.1.3.1;10.3.1 Simulating OWL import;225
6.1.3.2;10.3.2 Beyond OWL;228
6.1.4;10.4 Reasoning in modular ontologies;230
6.1.4.1;10.4.1 Atomic concepts and relations;230
6.1.4.2;10.4.2 Preservation of Boolean operators;230
6.1.4.3;10.4.3 Compilation and integrity;232
6.1.5;10.5 Conclusions;233
6.2;11 Evolution management;236
6.2.1;11.1 Change detection and classification;237
6.2.1.1;11.1.1 Determining harmless changes;237
6.2.1.2;11.1.2 Characterizing changes;238
6.2.1.3;11.1.3 Update management;240
6.2.2;11.2 Application in a case study;241
6.2.2.1;11.2.1 The WonderWeb case study;241
6.2.2.2;11.2.2 Modularization in the case study;243
6.2.2.3;11.2.3 Updating the models;244
6.2.3;11.3 Conclusions;245
7;Part V Conclusions;247
7.1;12 Conclusions;248
7.1.1;12.1 Lessons learned;248
7.1.2;12.2 Assumptions and Limitations;251
7.1.2.1;12.2.1 Shared Vocabularies;251
7.1.2.2;12.2.2 On demand translation;252
7.1.2.3;12.2.3 Modular Ontologies;253
7.1.3;12.3 Where are we now?;254
7.1.4;12.4 Is that all there is?;255
8;A Proofs of theorems;258
8.1;A.1 Theorem 6.6;258
8.2;A.2 Theorem 6.11;258
8.3;A.3 Theorem 6.14;259
8.4;A.4 Theorem 10.9;259
8.5;A.5 Theorem 10.11;259
8.6;A.6 Lemma 11.1;262
8.7;A.7 Theorem 11.2;262
9;References;263
10;Index;277