Ontology Matching

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Springer Science & Business Media, Jun 15, 2007 - Computers - 342 pages
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Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaiko’s book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, artificial intelligence. With Ontology Matching, researchers and practitioners will find a reference book which presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can equally be applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a detailed account of matching techniques and matching systems in a systematic way from theoretical, practical and application perspectives.
 

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ontology mathesis......numeri ontologici

Contents

63 Mixed schemabased and instancebased systems
176
64 Metamatching systems
184
65 Summary
186
Evaluation of matching systems
193
72 Data sets for evaluation
198
73 Evaluation measures
203
74 Applicationspecific evaluation
213
75 Summary
216

The matching problem
29
22 Ontology language
36
23 Types of heterogeneity
40
24 Terminology
42
25 The ontology matching problem
44
26 Summary
56
Ontology matching techniques
58
Classifications of ontology matching techniques
61
32 Classification of matching approaches
63
33 Other classifications
70
34 Summary
72
Basic techniques
73
42 Namebased techniques
74
43 Structurebased techniques
92
44 Extensional techniques
105
45 Semanticbased techniques
110
46 Summary
115
Matching strategies
117
52 Similarity aggregation
121
53 Global similarity computation
126
54 Learning methods
133
55 Probabilistic methods
141
56 User involvement and dynamic composition
142
57 Alignment extraction
144
58 Summary
149
Systems and evaluation
151
Overview of matching systems
152
61 Schemabased systems
154
62 Instancebased systems
169
Representing explaining and processing alignments
217
Frameworks and formats representing alignments
219
82 Alignment frameworks
235
83 Ontology editors with alignment manipulation capabilities
241
84 Summary
243
Explaining alignments
245
92 Explanation approaches
247
93 A default explanation
249
94 Explaining basic matchers
251
95 Explaining the matching process
252
96 Arguing about correspondences
255
97 Summary
257
Processing alignments
259
101 Ontology merging
260
102 Ontology transformation
261
104 Mediation
262
105 Reasoning
264
107 Summary
265
Conclusions
266
Conclusions
269
112 Future challenges
270
113 Final words
274
Legends of figures
275
Running example
277
Exercises
289
References
297
Index
322
Copyright

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Page 311 - C. Clifton. SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks.
Page 298 - Jon Barwise and Jerry Seligman. Information flow: the logic of distributed systems, volume 44 of Cambridge tracts in theoretical computer science. Cambridge University Press, Cambridge (UK), 1997.
Page 305 - IP Fellegi and AB Sunter. A theory for record linkage. Journal of the American Statistical Association, 64:1183-1210, 1969.
Page 312 - Explaining answers from the semantic web: The inference web approach. Journal of Web Semantics 1(4), 397-413 (2004) 55.
Page 311 - Wen-Syan Li and Chris Clifton: "Semantic Integration in Heterogeneous Databases Using Neural Networks,
Page 320 - US), 2000. [52] [Wu and Palmer, 1994] Zhibiao Wu and Martha Palmer. Verb semantics and lexical selection. In Proc. 32nd Annual Meeting of the Association for Computational Linguistics (ACL), pages 133-138, Las Cruces (NM US), 1994.

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About the author (2007)

Jérôme Euzenat is senior research scientist at INRIA where he leads the Exmo team dedicated to computer-mediated exchanges of structured knowledge. He is supervising the "Heterogeneity" work package of the Knowledge web network of excellence which aims at structuring the European research community in ontology alignment and merging.

Pavel Shvaiko is a postdoc fellow at the Department of Information and Communication Technology (DIT) of the University of Trento (UniTn), Trento, Italy. In 2006, he finished his PhD on "Iterative Schema-based Semantic Matching". Currently, he works in a European research project on matching multiple schemas, classifications, ontologies as a solution to the semantic heterogeneity problem.

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