Abstract
Concept matching is important when heterogeneous data sources are to be merged for the purpose of knowledge sharing. It has many useful applications in areas such as schema matching, ontology matching, scientific knowledge management, e-commerce, enterprise application integration, etc. With the desire of knowledge sharing and reuse in these fields, merging commonly occurs among different organizations where the knowledge describing the same domain is to be matched. Due to the different naming conventions, granularity and the use of concepts in different contexts, a semantic approach to this problem is preferred in comparison to syntactic approach that performs matches based upon the labels only. We propose a concept matching method that initially does not consider labels when forming candidate matches, but rather utilizes structural information to take the context into account and detect complex matches. Real world knowledge representations (schemas) are used to evaluate the method.
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References
Madhavan, J., Bernstein, P.A., Rahm, E.: Genereic Schema Matching with Cupid. In: Proceedings of the International Conference on very Large Data Bases (2001)
Melnik, S., Molina-Garcia, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm. In: Proceedings of ICDE-02 (2002)
Noy, N.F.: Semantic integration: a survey of ontology-based approaches. SIGMOD Record 33(4), 65–70 (2004)
Giunchiglia, F., Shvaiko, P.: Semantic matching. In: Ontologies and Distributed Systems Workshop, IJCAI (2003)
Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. Journal on Data Semantics IV (2005)
Doan, A., Halevy, A.: Semantic integration research in the database community: A brief survey. AI Magazine (2005)
Bernstein, P.A., Melnik, S., Churchill, J.E.: Incremental schema matching. In: Proc. of the 32nd Int’l Conf. on Very Large Data Bases, pp. 1167–1170 (2006)
Drumm, C., Schmitt, M., Do, H.: QuickMig - Automatic Schema Matching for Data Migration Projects. In: Proc. ACM CIKM, Lisabon (November 2007)
Amarintrarak, N., Runapongsa, S.K., Tongsima, S., Wiwatwattana, N.: SAXM: Semi-automatic XML Schema Mapping. In: The 24th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC (2009)
Sheth, A., Larson, J.: Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Comp. Surveys 22(3), 183–230 (1990)
Das, S., Chong, E.I., Eadon, G., Srinivasan, J.: Supporting ontology-based semantic matching in RDBMS. In: Proc. of 13th VLDB Conf., pp. 1054–1065 (2004)
Ge, J., Qiu, Y.: Concept Similarity Matching Based on Semantic Distance. In: Proc. of the 2008 4th Int’l Conf. on Semantics, Knowledge and Grid, SKG (2008)
Cruz, I.F., Antonelli, F.P., Stroe, C.: AgreementMaker: Efficient Matching for Large Real-World Schemas and Ontologie. In: Proceedings of VLDB, Demo (2009)
Miller, G.A.: WordNet: A Lexical Database for English. Comm. of the ACM 38(11) (1995)
Tan, H., Dillon, T.S., Hadzic, F., Feng, L., Chang, E.: IMB3-Miner: Mining Induced/Embedded Subtrees by Constraining the Level of Embedding. In: Proc. of the 10th Pacific-Asia Conf. on Knowledge Discovery and Data Mining (2006)
Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-Match: an Algorithm and an Implementation of Semantic Matching. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 61–75. Springer, Heidelberg (2004)
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Pan, Q.H., Hadzic, F., Dillon, T.S. (2010). Discovering Concept Mappings by Similarity Propagation among Substructures. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_40
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DOI: https://doi.org/10.1007/978-3-642-15381-5_40
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