دانلود کتاب Ontology Matching

فهرست مطالب :

Ontology Matching......Page 3
Preface......Page 6
About Ontology Matching......Page 7
Novelty of the Second Edition......Page 8
Outline of the Book......Page 9
Readership and Lecture Guide......Page 10
Acknowledgements......Page 11
Contents......Page 13
Part I: The Matching Problem......Page 16
1.1 Ontology Engineering......Page 17
Usage Examples......Page 0
1.1.1 Ontology Editing and Import......Page 18
1.2 Information Integration......Page 19
1.2.2 Catalogue Integration......Page 22
1.2.3 Data Integration......Page 23
1.3 Linked Data......Page 25
1.4 Peer-to-Peer Information Sharing......Page 27
1.4.1 Semantic P2P Systems......Page 28
1.4.2 Emergent Semantics Between Peers......Page 29
1.5 Web Service Composition......Page 30
1.6 Autonomous Communication Systems......Page 32
1.6.2 Matching Contexts in Ambient Computing......Page 33
1.7 Navigation and Query Answering on the Web......Page 34
1.7.1 Navigation on the Semantics Web......Page 35
1.7.2 Query Answering on the Web......Page 36
1.8 Summary......Page 37
2.1 Vocabularies, Schemas and Ontologies......Page 39
2.1.1 Tags and Folksonomies......Page 40
2.1.2 Directories......Page 41
2.1.3 Relational Database Schemas......Page 42
2.1.4 XML Schemas......Page 43
2.1.5 Conceptual Models......Page 45
2.1.6 Ontologies......Page 46
2.2 Ontology Language......Page 47
2.2.1 Ontology Entities......Page 48
2.2.2 Ontology Language Semantics......Page 50
2.3 Types of Heterogeneity......Page 51
2.4 Terminology......Page 53
2.5.1 The Matching Process......Page 55
2.5.2 Structure of an Alignment......Page 56
2.5.3 Towards a Semantics for Matching and Alignments......Page 62
2.6 Summary......Page 67
Chapter 3: Methodology......Page 69
3.1 The Alignment Life Cycle......Page 70
3.2 Identifying Ontologies and Characterising Needs......Page 71
3.3 Retrieving Existing Alignments......Page 74
3.4 Selecting and Composing a Matcher......Page 75
3.5 Matching Ontologies......Page 77
3.6 Evaluating Alignments......Page 78
3.7 Enhancing Alignments......Page 80
3.8 Storing and Sharing......Page 81
3.10 Summary......Page 82
Part II: Ontology Matching Techniques......Page 84
4.1 Matching Dimensions......Page 85
4.1.2 Process Dimensions......Page 86
4.2.1 Methodology......Page 87
4.2.2 Granularity/Input Interpretation Layer......Page 88
4.2.3 Origin/Kind of Input Layer......Page 90
String-Based Techniques......Page 91
Formal Resource-Based Techniques......Page 92
Model-Based Techniques......Page 93
4.4 Other Classifications......Page 94
4.5 Summary......Page 95
5.1 Similarity, Distances and Other Measures......Page 97
5.2 Name-Based Techniques......Page 99
Normalisation......Page 100
Substring Test......Page 101
Edit Distance......Page 102
Token-Based Distances......Page 105
Summary on String-Based Methods......Page 107
5.2.2 Language-Based Methods......Page 108
Intrinsic Methods: Linguistic Normalisation......Page 109
Extrinsic Methods......Page 110
Multilingual Methods......Page 116
5.3 Internal Structure-Based Techniques......Page 118
5.3.2 Data Type Comparison......Page 120
5.3.3 Domain Comparison......Page 121
5.3.4 Comparing Multiplicities and Properties......Page 122
5.4 Extensional Techniques......Page 124
5.4.1 Common Extension Comparison......Page 125
Formal Concept Analysis......Page 126
Linkkey Extraction......Page 127
5.4.3 Disjoint Extension Comparison......Page 129
Similarity-Based Extension Comparison......Page 130
Matching-Based Comparison......Page 131
5.5 Summary......Page 132
6.1 Relational Techniques......Page 133
6.1.1 Taxonomic Structure......Page 136
6.1.3 Relations......Page 139
6.1.4 Pattern-Based Matching......Page 140
6.2 Iterative Similarity Computation......Page 142
6.2.1 Similarity Flooding......Page 144
6.2.2 Similarity Equation Fixed Point......Page 146
6.3.1 Expectation Maximisation......Page 149
6.3.2 Particle Swarm Optimisation......Page 151
6.4.1 Bayesian Networks......Page 152
6.4.2 Markov Networks and Markov Logic Networks......Page 155
Summary on Probabilistic Matching......Page 156
6.5.1 Propositional Techniques......Page 157
6.5.2 Description Logic Techniques......Page 158
Summary on Semantic Techniques......Page 159
6.6 Summary......Page 160
7.1 Ontology Partitioning and Search-Space Pruning......Page 161
7.1.1 Partitioning......Page 162
7.1.2 Search-Space Pruning......Page 164
7.2 Matcher Composition......Page 165
7.3 Context-Based Matching......Page 168
Triangular Norms......Page 172
Multidimensional Distances and Weighted Sums......Page 174
Fuzzy Aggregation and Weighted Average......Page 176
Harmonic Adaptive Weighted Sum......Page 177
7.4.2 Voting......Page 178
Dempster-Shafer Theory......Page 179
7.4.3 Arguing......Page 181
Summary on Similarity and Alignment Aggregation......Page 183
7.5 Matching Learning......Page 184
7.5.1 Bayes Learning......Page 185
7.5.2 WHIRL Learner......Page 186
7.5.3 Neural Networks......Page 187
7.5.4 Support Vector Machines......Page 189
7.5.5 Decision Trees......Page 190
Summary on Matcher Learning......Page 191
7.6 Matcher Tuning......Page 192
7.6.1 Stacked Generalisation......Page 194
7.6.2 Genetic Algorithms......Page 196
7.7 Alignment Extraction......Page 198
7.7.1 Thresholds......Page 199
7.7.2 Strengthening and Weakening......Page 201
7.7.3 Optimising the Result......Page 202
7.8 Alignment Improvement......Page 204
7.8.1 Alignment Disambiguation......Page 205
7.8.2 Alignment Debugging......Page 206
7.9 Summary......Page 208
Part III: Systems and Evaluation......Page 210
Chapter 8: Overview of Matching Systems......Page 211
8.1.2 Hovy (University of Southern California)......Page 213
8.1.3 TransScm (Tel Aviv University)......Page 214
8.1.5 SKAT and ONION (Stanford University)......Page 215
8.1.6 Artemis (University of Milan and University of Modena e Reggio Emilia)......Page 216
8.1.7 H-Match (University of Milan)......Page 217
8.1.9 Anchor-Prompt (Stanford Medical Informatics)......Page 218
8.1.10 OntoBuilder (Technion Israel Institute of Technology)......Page 219
8.1.12 COMA and COMA++ (University of Leipzig)......Page 220
8.1.13 QuickMig (SAP, University of Leipzig)......Page 221
8.1.15 XClust (National University of Singapore)......Page 222
8.1.18 S-Match (University of Trento)......Page 223
8.1.19 HCONE (University of the Aegean)......Page 225
8.1.20 MoA (Electronics and Telecommunication Research Institute, ETRI)......Page 226
8.1.22 Stroulia & Wang (University of Alberta)......Page 227
8.1.23 MWSDI (University of Georgia)......Page 228
8.1.25 BayesOWL and BN Mapping (University of Maryland)......Page 229
8.1.26 OMEN (The Pennsylvania State University and Stanford University)......Page 230
8.1.28 HSM (Hong Kong University of Science and Technology, City University of Hong Kong)......Page 231
8.1.30 GeRoMeSuite (RWTH Aachen University)......Page 232
8.1.32 Scarlet (The Open University)......Page 233
8.1.33 OMviaUO (University of Genova, Universidad Politécnica de Valencia)......Page 234
8.1.34 BLOOMS/BLOOMS+ (Wright State University, Accenture Technology Labs and Ontotext AD)......Page 235
8.1.36 Elmeleegy and Colleagues (Purdue University)......Page 236
8.1.37 BeMatch (Versailles Saint-Quentin en Yvelines, University of Cauca)......Page 237
8.1.39 MatchPlanner (University of Montpellier)......Page 238
8.1.41 Lily (Southeast University, Nanjing University)......Page 239
8.1.43 Homolonto (University of Lausanne, Swiss Institute of Bioinformatics)......Page 240
8.1.45 MapPSO (FZI Research Center for Information Technology, Griffith University)......Page 241
8.1.46 TaxoMap (University of Paris-Sud 11, INRIA)......Page 242
8.2.1 T-tree (INRIA Rhône-Alpes)......Page 243
8.2.3 FCA-Merge (University of Karlsruhe)......Page 244
8.2.5 GLUE (University of Washington)......Page 245
8.2.6 iMAP (University of Illinois and University of Washington)......Page 246
8.2.8 SBI&NB (The Graduate University for Advanced Studies)......Page 247
8.2.10 Dumas (Technische Universität Berlin and Humboldt-Universität zu Berlin)......Page 248
8.2.11 Wang and Colleagues (Hong Kong University of Science and Technology and Microsoft Research Asia)......Page 249
8.2.12 sPLMap (University of Duisburg-Essen, and ISTI-CNR)......Page 250
8.2.14 VSBM & GBM (École Centrale Paris)......Page 251
8.3.1 SEMINT (Northwestern University, NEC and The MITRE Corporation)......Page 252
8.3.2 IF-Map (University of Southampton and University of Edinburgh)......Page 253
8.3.4 oMap (CNR Pisa)......Page 254
8.3.6 Wise-Integrator (SUNY at Binghamton, University of Illinois at Chicago and University of Louisiana at Lafayette)......Page 255
8.3.7 IceQ (University of Illinois at Urbana-Champaign, University of Illinois at Chicago, SUNY at Binghamton)......Page 256
8.3.8 OLA (INRIA Rhône-Alpes and Université de Montréal)......Page 257
8.3.9 Falcon-AO (China Southeast University)......Page 258
8.3.10 RiMOM (Tsinghua University)......Page 259
8.3.11 Corpus-Based Matching (University of Washington, Microsoft Research and University of Illinois)......Page 260
8.3.12 iMapper (Norwegian University of Science and Technology)......Page 261
8.3.14 AROMA (University of Nantes, INRIA)......Page 262
8.3.16 SeMap (Georgia Tech, University of British Columbia)......Page 263
8.3.17 ASMOV (INFOTECH Soft, Inc., University of Miami)......Page 264
8.3.19 Smart Matcher (Vienna University of Technology)......Page 265
8.3.20 GEM/Optima/Optima+ (University of Georgia, Wright State University)......Page 266
8.3.21 CSR (University of the Aegean, Institution of Informatics and Telecommunications)......Page 267
8.3.23 YAM & YAM++ (University of Montpellier, University of Toronto)......Page 268
8.3.24 MoTo (University of Bari)......Page 269
8.3.26 LogMap (University of Oxford)......Page 270
8.4.1 APFEL (University of Karlsruhe and University of Koblenz-Landau)......Page 272
8.4.2 LCS (Queen\'s University Belfast)......Page 273
8.4.4 eTuner (University of Illinois and The MITRE Corporation)......Page 274
8.4.5 mSeer (University of Wisconsin-Madison, The MITRE Corporation)......Page 275
8.4.6 GOALS (Polytechnic of Porto)......Page 276
8.4.8 SMB (Technion Israel Institute of Technology)......Page 277
8.4.10 AMS (SAP Research, Dresden University of Technology, University of Leipzig)......Page 278
8.5 Summary......Page 279
9.1.1 Goals......Page 294
9.1.2 Principles......Page 295
Text REtrieval Conference......Page 296
Ontology Alignment Evaluation Initiative......Page 297
9.1.4 Types of Evaluations......Page 298
9.1.5 Automation......Page 299
9.2.1 Dimensions and Variability of Alignment Evaluation......Page 300
Input Ontologies......Page 301
Parameters and Resources......Page 302
Output Alignment......Page 303
9.2.2 Examples of Data Sets......Page 304
Large Scale Ontology Sets......Page 305
Other Test Collections......Page 306
9.2.3 Test Generation......Page 307
9.3.1 Compliance Measures......Page 309
9.3.2 Generalising Precision and Recall......Page 314
Weighted Precision and Recall......Page 315
Relaxed Precision and Recall......Page 316
Semantic Precision and Recall......Page 318
9.3.3 Sampling and Relative Precision and Recall......Page 319
Scalability......Page 321
General Subjective Satisfaction......Page 322
9.4.1 Aggregating Measures......Page 323
9.4.2 Evaluation Setting......Page 325
9.5 Summary......Page 326
Part IV: Representing, Explaining, and Processing Alignments......Page 327
10.1 Alignment Formats......Page 328
10.1.1 MAFRA Semantic Bridge Ontology (SBO)......Page 330
10.1.2 OWL......Page 331
10.1.3 Contextualized OWL (C-OWL)......Page 333
10.1.4 SWRL and RIF......Page 334
10.1.5 Alignment Format......Page 336
10.1.6 Expressive and Declarative Ontology Alignment Language (EDOAL)......Page 338
Mapping Vocabulary......Page 341
10.1.8 Comparison of Existing Formats......Page 342
10.2 Alignment Metadata......Page 344
10.2.2 Provenance Metadata......Page 345
10.3 Alignment Frameworks......Page 347
10.3.1 Model Management......Page 348
10.3.2 COMA++ (University of Leipzig)......Page 349
10.3.4 MAFRA (Instituto Politecnico do Porto and University of Karlsruhe)......Page 351
10.3.5 The Protégé Prompt Suite (Stanford University)......Page 352
Classes......Page 353
Functions......Page 354
10.3.7 FOAM (University of Karlsruhe)......Page 355
10.3.8 Harmony (The MITRE Corporation)......Page 356
10.4 Summary......Page 357
11.1 Individual Matching......Page 359
11.1.2 Manual Matcher Composition......Page 360
11.1.3 Relevance Feedback......Page 361
11.2 Collective Matching......Page 363
11.2.1 Community-Driven Ontology Matching......Page 364
11.2.2 Crowdsourcing Ontology Matching......Page 365
11.3.1 Explanation Approaches......Page 366
The Proof Presentation Approach......Page 367
The S-Match Example......Page 368
An Argumentation Example......Page 370
11.3.3 Explaining Basic Matchers......Page 371
Dependency Graphs......Page 372
Explaining Logical Reasoning......Page 373
11.4 Alignment Editors and Visualisers......Page 375
11.4.1 WSMT (DERI, University of Innsbruck)......Page 376
11.4.3 iMerge (Duisbourg University)......Page 377
11.4.4 Chimaera (Stanford University)......Page 378
11.4.6 AlViz (Vienna University of Technology, Norwegian University of Science and Technology)......Page 379
11.4.7 CogZ (University of Victoria)......Page 380
11.5 Summary......Page 381
Chapter 12: Processing Alignments......Page 382
12.1 Ontology Merging......Page 383
12.2 Ontology Transformation......Page 385
12.3 Data Translation......Page 386
12.3.1 Clio (IBM Almaden and University of Toronto)......Page 388
12.3.2 Spicy (University of Basilicata, ICAR-CNR)......Page 389
12.4.1 KnoFuss (The Open University)......Page 390
12.4.2 Silk (Chemnitz University of Technology, Freie Universität Berlin)......Page 391
12.5 Mediation......Page 392
12.6 Reasoning......Page 394
12.7 Alignment Services and Repositories......Page 395
12.7.2 Alignment Server (INRIA)......Page 397
12.7.3 CATCH (Vrije Universiteit Amsterdam)......Page 398
12.8.1 ToMAS (University of Toronto and IBM Almaden)......Page 399
12.9 Summary......Page 400
Part V: Conclusions......Page 402
13.1 A Brief Outlook of the Trends in the Field......Page 403
13.2.1 Large-Scale and Efficient Matching......Page 405
13.2.4 User Involvement......Page 406
13.2.6 Uncertainty in Ontology Matching......Page 407
13.3 Final Words......Page 408
Appendix A: Legends of Figures......Page 410
Appendix B: Running Example......Page 412
B.4.1 refalign.rdf......Page 421
B.4.2 nearmiss.rdf......Page 422
B.4.3 farone.rdf......Page 423
B.4.4 noncomplete.rdf......Page 424
Appendix C: Exercises......Page 426
Appendix D: Solutions......Page 434
References......Page 465
Index......Page 498