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minimally inconsistent sets by the support of an external Description Logic reasoner has been proposed in Schlobach and Huang (2005). That approach has been prototypically implemented as the DION (Debugger of Inconsistent Ontologies). DION uses the relevance relation which has been used in PION as its heuristic information to guide the selecting procedure for nding minimally inconsistent sets. That justi es to some extent that the notion of concept relevance is useful for inconsistent ontology processing. In future work, we are going to test PION with more large-scale ontology examples. We are also going to investigate different approaches for selection functions (e.g., semantic-relevance based) and different extension strategies as alternatives to the linear extension strategy in combination with different selection functions, and test their performance.
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We are indebted to Peter Haase for so carefully proofreading this chapter.
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Alchourron C, Gaerdenfors P, Makinson D. 1985. On the logic of theory change: partial meet contraction and revision functions. The Journal of Symbolic Logic 50: 510 530. Belnap N. 1977. A useful four-valued logic. In Modern Uses of Multiple-Valued Logic, Reidel, Dordrecht, pp 8 37. Benferhat S, Garcia L. 2002. Handling locally strati ed inconsistent knowledge bases, Studio Logica, 77 104. Beziau J-Y. 2000. What is paraconsistent logic. In Frontiers of Paraconsistent Logic. Research Studies Press: Baldock, pp 95 111. Budanitsky A, Hirst G. 2001. Semantic distance in wordnet: An experimental, application-oriented evaluation of ve measures. In Workshop on WordNet and Other Lexical Resources, Pittsburgh, PA. Chopra S, Parikh R, Wassermann R. 2000. Approximate belief revision-prelimininary report. Journal of IGPL. Flouris G, Plexousakis D. Antoniou G. 2005. On applying the agm theory to dls and owl. In International Semantic Web Conference, LNCS, Springer verlag. Friedrich G, Shchekotykhin K. 2005. A general diagnosis method for ontologies. In International Semantic Web Conference, LNCS, Springer Verlag. Hameed A, Preece A. Sleeman D. 2003. Ontology reconciliation. In Handbook on Ontologies in Information Systems. Springer Verlag, pp 231 250. Huang Z, van Harmelen F, ten Teije A. 2005. Reasoning with inconsistent ontologies. In Proceedings of the International Joint Conference on Arti cial Intelligence - IJCAI 05, pp 454-459. Huang Z, Visser C. 2004. Extended DIG description logic interface support for PROLOG, Deliverable D3.4.1.2, SEKT.
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Lang J, Marquis P. 2001. Removing inconsistencies in assumption-based the-ories through knowledge-gathering actions. Studio, Logica, 179 214. Levesque HJ (1989). A Knowledge-level account of abduction. In Proceedings of IJCAI 89, pp 1061 1067. Marquis P, Porquet N. 2003. Resource-bounded paraconsistent inference. Annals of Mathematics and Arti cial Intelligence, 349 384. McGuinness D, van Harmelen F. 2004. Owl web ontology language, Recommendation, W3C. Reiter R. 1987. A theory of diagnosis from rst principles. Arti cial Intelligence Journal 32:57 96. Schaerf M, Cadoli M. 1995. Tractable reasoning via approximation. Arti cial Intelligence, 249 310. Schlobach S. 2005a. Debugging and semantic clari cation by pinpointing. In Proceedings of the European Semantic Web Symposium, Vol. 3532 of LNCS, Springer Verlag, pp 226 240. Schlobach S. 2005b. Diagnosing terminologies. In Proceedings of the Twentieth National Conference on Arti cial Intelligence, AAAI 05, AAAI, pp 670 675. Schlobach S, Cornet R. 2003. Non-standard reasoning services for the debugging of description logic terminologies. In Proceedings of IJCAI 2003 . Schlobach S, Huang Z. 2005 Inconsistent ontology diagnosis: Framework and prototype, Project Report D3.6.1, SEKT.
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Ontology Mediation, Merging, and Aligning
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On the Semantic Web, data is envisioned to be annotated using ontologies. Ontologies convey background information which enriches the description of the data and which makes the context of the information more explicit. Because ontologies are shared speci cations, the same ontologies can be used for the annotation of multiple data sources, not only Web pages, but also collections of XML documents, relational databases, etc. The use of such shared terminologies enables a certain degree of inter-operation between these data sources. This, however, does not solve the integration problem completely, because it cannot be expected that all individuals and organizations on the Semantic Web will ever agree on using one common terminology or ontology (Visser and Cui, 1998; Uschold, 2000). It can be expected that many different ontologies will appear and, in order to enable inter-operation, differences between these ontologies have to be reconciled. The reconciliation of these differences is called ontology mediation. Ontology mediation enables reuse of data across applications on the Semantic Web and, in general, cooperation between different organizations. In the context of semantic knowledge management, ontology mediation is especially important to enable sharing of data between heterogeneous knowledge bases and to allow applications to reuse data
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Semantic Web Technologies: Trends and Research in Ontology-based Systems John Davies, Rudi Studer, Paul Warren # 2006 John Wiley & Sons, Ltd
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