Towards the semantic web : ontology-driven knowledge management

Towards The Semantic Web: Ontology-Driven Knowledge Management......Page 1
Contents......Page 3
Dr John Davies......Page 13
Professor Dieter Fensel......Page 14
Professor Frank van Harmelen......Page 15
List of Contibutors......Page 17
Acknowledgements......Page 19
1 Introduction......Page 21
1.1 The Semantic Web and Knowledge Management......Page 22
1.2 The Role of Ontologies......Page 24
1.3.1 Knowledge Acquisition......Page 25
1.3.2 Knowledge Representation......Page 26
1.4 Tools for Semantic Web- based Knowledge Management......Page 27
1.4.4 Knowledge Use......Page 28
2.1 Introduction......Page 31
2.2.1 XML for Data Exchange......Page 32
2.2.2 RDF for Assertions......Page 33
2.2.3 RDF Schema for Simple Ontologies......Page 34
2.3 Design Rationale for OIL......Page 35
2.3.1 Frame- based Systems......Page 36
2.4 OIL Language Constructs......Page 37
2.4.1 A Simple Example in OIL......Page 38
2.5 Different Syntactic Forms......Page 40
2.6 Language Layering......Page 43
2.8.1 Integration with RDFS......Page 46
2.8.3 OIL Data Types DAML+......Page 49
2.9 Experiences and Future Developments......Page 51
3.1 Introduction......Page 53
3.2 Feasibility Study......Page 54
3.3 Kick Off Phase......Page 58
3.5 Evaluation Phase......Page 61
3.7 Related Work......Page 62
3.7.1 Skeletal Methodology......Page 63
3.7.3 Methontology......Page 64
3.8 Conclusion......Page 65
4.1 The Requirement for Ontology Management......Page 67
4.2.1 Why is Aligning Needed......Page 68
4.2.2 Aligning Annotated XML Documents......Page 69
4.2.3 Mapping Meta- ontology......Page 70
4.2.4 Mapping in OIL......Page 73
4.3.1 Ontologies are Changing......Page 74
4.3.2 Changes in Ontologies Involve Several Problems......Page 75
4.3.3 Change Management......Page 78
4.4 Organizing Ontologies......Page 81
4.4.2 Functionality of an Ontology Storage System......Page 82
4.4.3 Current Storage Systems......Page 84
4.4.4 Requirements for a Storage System......Page 86
4.5 Summary......Page 89
5 Sesame: A Generic Architecture for Storing and Querying RDF and RDF 
Schema......Page 91
5.1.1 Querying at the Syntactic Level......Page 92
5.1.2 Querying at the Structure Level......Page 93
5.1.3 Querying at the Semantic Level......Page 95
5.2 Sesame Architecture......Page 96
5.2.1 The RQL Query module......Page 98
5.2.2 The Admin Module......Page 99
5.3 The SAIL API......Page 100
5.4.1 Application: On- To- Knowledge......Page 102
5.4.3 PostgreSQL and SAIL......Page 104
5.4.4 MySQL......Page 106
5.5.1 Transaction Rollback Support......Page 107
5.6 Conclusions......Page 108
6.1 Introduction......Page 111
6.1.1 OntoBuilder and its Relation to the CORPORUM System......Page 112
6.1.2 OntoExtract......Page 113
6.1.3 OntoWrapper and TableAnalyser......Page 116
6.2.1 Semantics on the Internet......Page 117
6.2.2 Problems with Retrieving Natural Language Texts from Documents......Page 118
6.2.4 Normalization......Page 120
6.2.6 Document Class Categorization......Page 121
6.2.8 Layout Issues......Page 122
6.3.1 Content- driven Versus Goal- driven......Page 123
6.3.2 Levels of Linguistic Analysis......Page 124
6.3.3 CognIT Vision......Page 127
6.4.2 Generating Semantic Structures......Page 128
6.4.3 Generating Ontologies from Textual Resources......Page 130
6.4.4 Visualization and Navigation......Page 131
6.5 Issues in Using Automated Text Extraction for Ontology Building using IE on Web Resources......Page 132
7.1 Introduction......Page 137
7.2 Kick Off Phase......Page 138
7.3 Re . nement Phase......Page 143
7.3.1 Transaction Management......Page 144
7.3.2 Locking Sub- trees of the Concept Hierarchy......Page 146
7.3.3 What Does Locking a Concept Mean?......Page 147
7.4.1 Analysis of Typical Queries......Page 148
7.4.3 Usage of Competency Questions......Page 149
7.5 Related Work......Page 150
7.6 Conclusion......Page 151
8.1 Introduction......Page 153
8.2 Ontological Indexing......Page 155
8.3 Ontological Searching......Page 158
8.4.1 Indexing in the New Model......Page 161
8.5.1 Technical Enhancements......Page 162
8.6 Concluding Remarks......Page 163
9.2 Spectacle Content Presentation Platform......Page 165
9.2.1 Ontologies in Spectacle......Page 166
9.4 Ontology- based Mapping Methodology......Page 167
9.4.2 Ontology Mapping......Page 169
9.4.4 Navigation Speci . cation......Page 170
9.4.5 Navigation Rendering......Page 171
9.4.7 User Pro . les......Page 172
9.5.1 Analysis......Page 173
9.5.2 Querying......Page 176
9.5.3 Navigation......Page 178
9.6 Summary: Semantics- based Web Presentations......Page 179
10.1 Introduction......Page 181
10.2 Sharing and Retrieving Knowledge in OntoShare......Page 182
10.2.1 Sharing Knowledge in OntoShare......Page 183
10.2.2 Ontological Representation......Page 184
10.2.3 Retrieving Explicit Knowledge in OntoShare......Page 187
10.3 Creating Evolving Ontologies......Page 189
10.4 Expertise Location and Tacit Knowledge......Page 190
10.5.1 Tacit and Explicit Knowledge Flows......Page 192
10.5.2 Virtual Communities......Page 193
10.6 Evaluation and Further Work......Page 195
10.7 Concluding Remarks......Page 196
11.1 Ontology Middleware: Features and Architecture......Page 199
11.1.1 Place in the On- To- Knowledge Architecture......Page 201
11.1.2 Terminology......Page 202
11.2 Tracking Changes, Versioning and Meta- information......Page 203
11.2.2 Requirements......Page 204
11.3 Versioning Model for RDF( S) Repositories......Page 205
11.3.3 Implementation Approach......Page 208
11.3.4 Meta- information......Page 210
11.4 Instance Reasoning for OIL DAML+......Page 212
11.4.1 Inference Services......Page 214
11.4.2 Functional Interfaces to a OIL Reasoner DAML+......Page 215
12.1 Introduction......Page 217
12.2.1 What is Skills Management?......Page 218
12.2.2 SkiM: Skills Management at Swiss Life......Page 220
12.2.3 Architecture of SkiM......Page 222
12.2.4 SkiM as an Ontology- based Approach......Page 223
12.2.5 Querying Facilities......Page 227
12.2.6 Evaluation and Outlook......Page 228
12.3.1 Motivation......Page 229
12.3.2 Automatic Ontology Extraction......Page 230
12.3.3 Employing the Ontology for Querying......Page 233
12.3.4 Evaluation and Outlook......Page 235
12.4 Conclusions......Page 237
13.2 The EnerSearch Industrial Research Consortium as a Virtual Organization......Page 239
13.3 Why Might Semantic Web Methods Help?......Page 242
13.4 Design Considerations of Semantic Web Field Experiments......Page 243
13.4.2 Different Target User Groups......Page 244
13.4.3 Different Individual Cognitive Styles......Page 245
13.4.4 Hypotheses to be Tested......Page 248
13.5.1 Selecting Target Test Users......Page 249
13.5.3 Test Tasks and their Organization......Page 250
13.5.4 Experimental Procedure......Page 251
13.5.5 Determining What Data to Collect......Page 252
13.5.6 Evaluation Matrix and Measurements......Page 253
13.6.1 System Design......Page 254
13.6.2 Ontology Engineering, Population, Annotation......Page 255
13.7.1 Ontology and Semantic Sitemaps......Page 256
13.7.2 Semantics- based Information Retrieval......Page 259
13.8 Some Lessons Learned......Page 261
14.1 Introduction......Page 265
14.2.2 Knowledge Categorization......Page 267
14.2.4 Personalization......Page 268
14.2.6 Communities of Practice......Page 269
14.2.7 P2P Computing and its Implications for KM......Page 270
14.2.10 The Knowledge Grid......Page 271
14.3 A Vision of Ontologies: Dynamic Networks of Meaning......Page 272
14.3.1 Ontologies or How to Escape a Paradox......Page 273
14.3.2 Heterogeneity in Space: Ontology as Networks of Meaning......Page 274
14.3.3 Development in Time: Living Ontologies......Page 275
Paradigms......Page 276
14.4.2 Challenges in Integrating Peer- 2- Peer and Ontologies......Page 278
14.5.2 P2P for Ontologies......Page 283
14.5.4 Community Building......Page 284
15 Conclusions: Ontology-driven Knowledge Management – Towards the 
Semantic Web?......Page 285
References......Page 287
Index......Page 301