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Instance-Based Ontology Matching Using Different Kinds of Formalism

Authors: Katrin Zaiß, Tim Schlüter, Stefan Conrad


Ontology Matching is a task needed in various applica-tions, for example for comparison or merging purposes. In literature,many algorithms solving the matching problem can be found, butmost of them do not consider instances at all. Mappings are deter-mined by calculating the string-similarity of labels, by recognizinglinguistic word relations (synonyms, subsumptions etc.) or by ana-lyzing the (graph) structure. Due to the facts that instances are oftenmodeled within the ontology and that the set of instances describesthe meaning of the concepts better than their meta information,instances should definitely be incorporated into the matching process.In this paper several novel instance-based matching algorithms arepresented which enhance the quality of matching results obtainedwith common concept-based methods. Different kinds of formalismsare use to classify concepts on account of their instances and finallyto compare the concepts directly.KeywordsInstances, Ontology Matching, Semantic Web

Keywords: Semantic Web, ontology matching, Instances

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[1] A. Doan, J. Madhavan, P. Domingos, and A. Y. Halevy, OntologyMatching: A Machine Learning Approach, in Handbook on Ontologies.Springer, 2004, pp. 385404.
[2] H. H. Do and E. Rahm, COMA - A System for Flexible Combinationof Schema Matching Approaches, in VLDB 2002, Proceedings of 28thInternational Conference on Very Large Data Bases, August 20-23,2002, Hong Kong, China. Morgan Kaufmann, 2002, pp. 610621.
[3] D. Engmann and S. Mamann, Instance Matching with COMA++,in Datenbanksysteme in Business, Technologie und Web (BTW 2007),Workshop Proceedings, 5.-6. Marz 2007, Aachen, Germany, 2007.
[4] J. Berlin and A. Motro, Database Schema Matching Using MachineLearning with Feature Selection, in Advanced Information SystemsEngineering, 14th International Conference, CAiSE 2002, Toronto,Canada, May 27-31, 2002, Proceedings, 2002, pp. 452466.
[5] A. Bilke and F. Naumann, Schema Matching Using Duplicates, inProceedings of the 21st International Conference on Data Engineering,ICDE 2005, 5-8 April 2005, Tokyo, Japan, 2005, pp. 6980.
[6] G. Stumme and A. Maedche, FCA-MERGE: Bottom-Up Mergingof Ontologies, in Proceedings of the Seventeenth International JointConference on Artificial Intelligence, IJCAI 2001, Seattle, Washington,USA, August 4-10, 2001, 2001, pp. 225234.
[7] A. Doan, P. Domingos, and A. Y. Levy, Learning Source Descriptionfor Data Integration, in WebDB (Informal Proceedings), 2000.
[8] M. S. Lacher and G. Groh, Facilitating the Exchange of ExplicitKnowledge through Ontology Mappings, in Proceedings of the Four-teenth International Florida Artificial Intelligence Research SocietyConference, May 21-23, 2001, Key West, Florida, USA, 2001.
[9] J. Euzenat and P. Shvaiko, Ontology Matching. Heidelberg (DE):Springer-Verlag, 2007.
[10] M. Ehrig and S. Staab, QOM - Quick Ontology Mapping. in IN-FORMATIK 2004 - Informatik verbindet, Band 1, Beitrage der 34.Jahrestagung der Gesellschaft fur Informatik e.V. (GI), Ulm, 20.-24.September 2004. GI, 2004, pp. 356361.
[11] J. Rothe, Complexity Theory and Cryptology. Secaucus, NJ, USA:Springer-Verlag New York, Inc., 2005.Fig. 5. Detailed results for Tests 2 and 3
[12] M. Habibi, Real World Regular Expressions with Java 1.4. APress,2004.
[13] K. Zai, T. Schlueter, and S. Conrad, Instance-based ontology matchingusing regular expressions, in OTM Workshops, ser. Lecture Notes inComputer Science, R. Meersman, Z. Tari, and P. Herrero, Eds., vol.5333. Springer, 2008, pp. 4041.
[14] E. M. Gold, Language Identification in the Limit, Information andControl, vol. 10, no. 5, pp. 447474, 1967. (Online). Available:
[15] H. Fernau, Algorithms for Learning Regular Expressions, in ALT,ser. Lecture Notes in Computer Science, S. Jain, H.-U. Simon, andE. Tomita, Eds., vol. 3734. Springer, 2005, pp. 297311.
[16] G. J. Bex, W. Gelade, F. Neven, and S. Vansummeren, Learning Deter-ministic Regular Expressions for the Inference of Schemas from XMLData, in WWW 08: Proceeding of the 17th international conferenceon World Wide Web. New York, NY, USA: ACM, 2008, pp. 825834.
[17] K. Zai, Entwicklung eines Frameworks fuer instanzbasiertesOntologie-Matching (Developing a Framework for instance-based On-tology Matching) , in Tagungsband zum 20. GI-Workshop uber Grund-lagen von Datenbanken (20th GI-Workshop on the Foundations ofDatabases), Apolda, Thringen, 13.-16. Mai 2008, 2008.
[18] H. H. Do and E. Rahm, Coma - a system for flexible combinationof schema matching approaches. in VLDB 2002, Proceedings of 28thInternational Conference on Very Large Data Bases, August 20-23,2002, Hong Kong, China, 2002, pp. 610621.
[19] M. Kay and M. Roscheisen, Text-translation alignment. in Computa-tional Linguistics, vol. 19, no. 1, 1993, pp. 121142.
[20] Ontology Aligment Evaluation Initiative - 2007 Campaign,, 2007.
[21] The DBLP Computer Science Bibliography, ley/db/, June 2008.