Information sharing on the semantic web 2003

Heiner Stuckenschmidt, Frank van Harmelen Information Sharing on the Semantic Web SPIN – Draft – December 1, 2003 Springer Berlin Heidelberg NewYork Hong Kong London Milan Paris Tokyo To absent friends Preface People that contributed to the work summarized in this monograph: • • • • • • • • • • Fausto Giunchiglia, DIT, University of Trento, Italy Jens Hartmann, AIFB, Univerity of Karlsruhe, Germany Catholijn Jonker, Vrije Universiteit Amsterdam, The Netherlands Michel Klein, Vrije Universiteit Amsterdam, The Netherlands Eduardo Mena, University of Saragoza, Spain Christoph Schlieder, University of Bamberg, Germany Tim Verwaart, LEI Wageningen, The Netherlands Ubbo Visser, TZI, University of Bremen, Germany Thomas Voegele, TZI, University of Bremen, Germany Holger Wache, TZI, University of Bremen, Germany Amsterdam, month year Heiner Stuckenschmidt Frank van Harmelen Contents Part I Information Sharing Semantic Integration 1.1 Syntactic Standards 1.1.1 HTML: Visualizing Information 1.1.2 XML: Exchanging Information 1.1.3 RDF: A Data-Model for Meta-Information 1.1.4 The Roles of XML and RDF 1.2 Handling Information Semantics 1.2.1 Semantics from Structure 1.2.2 Semantics from Text 1.2.3 The Need for Explicit Semantics 1.3 Representing and Comparing Semantics 1.3.1 Names and Labels 1.3.2 Term Networks 1.3.3 Concept Lattices 1.3.4 Features and Constraints 1.4 An Example: Water Quality Assessment 1.4.1 Functional Transformation 1.4.2 Non-Functional Transformations 1.5 Conclusion 3 4 7 10 12 13 13 14 15 16 16 17 19 Ontology-Based Information Sharing 2.1 Ontologies 2.1.1 Shared Vocabularies and Conceptualizations 2.1.2 Specification of Context Knowledge 2.1.3 Beneficial Applications 2.2 Ontologies in Information Integration 2.2.1 Content Explication 2.2.2 Additional Roles of Ontologies 2.3 Ontological Engineering 21 21 22 23 24 26 27 30 32 X Contents 2.3.1 Development Methodology 2.3.2 Supporting tools 2.3.3 Ontology Evolution 2.4 Conclusions 32 35 36 37 Part II Semantic Web Infrastructure Ontology Languages for the Semantic Web 3.1 RDF Schema 3.2 The Web Ontology Language OWL 3.3 Other Web-Based Ontology Languages 3.3.1 Semantic Web Languages 3.3.2 Comparison and Results 3.3.3 A unifying View 3.4 Conclusions 41 41 41 41 41 42 44 45 Ontology Creation 4.1 Ontologies and Knowledge Integration 4.1.1 The Explication Dilemma 4.1.2 Avoiding the Explication Dilemma 4.2 A Translation Approach to Ontology Alignment 4.2.1 The Translation Process 4.2.2 Required Infrastructure 4.2.3 Building the Infrastructure 4.3 Applying the Approach 4.3.1 The Task to be Solved 4.3.2 The Information Sources 4.3.3 Sources of Knowledge 4.4 An Example Walkthrough 4.5 Conclusions 47 47 48 49 50 50 51 53 56 56 57 59 61 67 Metadata Generation 5.1 The Role of Metadata 5.1.1 Use of Meta-Data 5.1.2 Problems with Metadata Management 5.2 The WebMaster Approach 5.2.1 BUISY: A Web-Based Environmental Information System 5.2.2 The WebMaster Workbench 5.2.3 Applying WebMaster to the BUISY System 5.3 Learning Classification Rules 5.3.1 Inductive Logic Programming 5.3.2 Applying Inductive Logic Programming 5.3.3 Learning Experiments 69 70 71 72 73 74 75 77 81 82 83 86 Contents 5.3.4 Extracted Classification Rules 5.4 Ontology Deployment 5.4.1 Generating Ontology-Based Metadata 5.4.2 Using Ontology-based Metadata 5.5 Conclusions XI 90 94 94 96 97 Part III Retrieval, Integration and Querying Retrieval and Integration 101 6.1 Semantic Integration 102 6.1.1 Ontology Heterogeneity 102 6.1.2 Multiple Systems and Translatability 103 6.1.3 Approximate Re-classification 104 6.2 Concept-Based Filtering 106 6.2.1 The Idea of Query-Rewriting 107 6.2.2 Boolean Concept Expressions 108 6.2.3 Query Re-Writing 110 6.3 Processing Complex Queries 113 6.3.1 Queries as Concepts 113 6.3.2 Query Relaxation 115 6.4 Examples from a Case Study 118 6.4.1 Concept Approximations 118 6.4.2 Query Relaxation 119 6.5 Conclusions 120 Sharing Statistical Information 123 7.1 The Nature of Statistical Information 124 7.1.1 Statistical Metadata 124 7.1.2 A Basic Ontology of Statistics 126 7.2 Modelling Statistics 129 7.2.1 Statistics as Views 129 7.2.2 Connection with the Domain 131 7.3 Translation to Web Languages 134 7.3.1 Ontologies 135 7.3.2 Description of Information 139 7.4 Retrieving Statistical Information 142 7.5 Conclusions 144 Spatially-Related Information 147 8.1 Spatial Representation and Reasoning 147 8.1.1 Levels of spatial abstraction 148 8.1.2 Reasoning about spatial relations 149 8.2 Ontologies and Spatial Relevance 150 8.2.1 Defining Spatial Relevance 150 XII Contents 8.2.2 Combined Spatial and Terminological Matching 152 8.2.3 Limitations 154 8.3 Graph-Based Reasoning about spatial relevance 155 8.3.1 Partonomies 156 8.3.2 Topology 157 8.3.3 Directions 159 8.3.4 Distances 160 8.4 Conclusions 161 Integration and Retrieval Systems 163 9.1 OntoBroker 164 9.1.1 F-Logic and its Relation to OWL 165 9.1.2 Ontologies, Sources and Queries 167 9.1.3 Context Transformation 168 9.2 OBSERVER 170 9.2.1 Query Processing in OBSERVER 171 9.2.2 Vocabulary Integration 172 9.2.3 Query Plan Generation and Selection 175 9.3 The BUSTER System 176 9.3.1 The Use of Shared Vocabularies 178 9.3.2 Retrieving Accommodation Information 179 9.3.3 Spatial and Temporal Information 180 9.4 Conclusions 184 Part IV Distributed Ontologies 10 Modularization 187 10.1 Motivation 187 10.1.1 Requirements 188 10.1.2 Our Approach 189 10.1.3 Related Work 189 10.2 Modular Ontologies 191 10.2.1 Syntax and Architecture 191 10.2.2 Semantics and Logical Consequence 193 10.3 Comparison with OWL 195 10.3.1 Resembling OWL Import 195 10.3.2 Beyond OWL 198 10.4 Reasoning in Modular Ontologies 200 10.4.1 Atomic Concepts and Relations 200 10.4.2 Preservation of Boolean Operators 201 10.4.3 Compilation and Integrity 203 10.5 Conclusions 204 ... two systems Semantic Integration not use the same interpretation of the information The simplest form of disagreement in the interpretation of information are homonyms (the use of the same word... to capture information structures as well as meta -information about the nature of information and the conceptual structure underlying an information source Ontologies: The information sources... Explication The other comparison criterion is the extend of explication that is reached by the ontology This criterion is strongly connected with the expressive power of the specification language