Semantic matching: given two graph representations of ontologies G1 and G2, compute N1 × N2 mapping elements 〈IDi,j, n1i, n2j, R′〉 , with n1i ∈ G1, i = 1,...,N1, n2j ∈ G2, j = 1,...,N2 and R′ the strongest semantic relation which is supposed to hold between the concepts at nodes n1i and n2j.
A mapping element is a 4-tuple 〈IDij, n1i, n2j, R〉, i = 1,...,N1; j = 1,...,N2; where IDij is a unique identifier of the given mapping element; n1i is the i-th node of the first graph, N1 is the number of nodes in the first graph; n2j is the j-th node of the second graph, N2 is the number of nodes in the second graph; and R specifies a semantic relation which is supposed to hold between the concepts at nodes n1i and n2j.
The semantic relations are within equivalence (=), more general (⊒), less general (⊑), disjointness (⊥) and overlapping (⊓). When none of the above mentioned relations can be explicitly computed, the special idk(I don’t know) relation is returned. The relations are...
- 1.Avesani P., Giunchiglia F., and Yatskevich M. A large scale taxonomy mapping evaluation. In Proc. Fourth Int. Semantic Web Conf., 2005, pp. 67–81.Google Scholar
- 2.Batini C., Lenzerini M., and Navathe S. A comparative analysis of methodologies for database schema integration. ACM Comput. Surv., 18(4):323–364, 1986.Google Scholar
- 3.Bernstein P., Melnik S., Petropoulos M., and Quix C. Industrial-strength schema matching. ACM SIGMOD Rec., 33(4):38–43, 2004.Google Scholar
- 4.Bouquet P., Serafini L., and Zanobini S. Semantic coordination: a new approach and an application. In Proc. Second Int. Semantic Web Conf., 2003, pp. 130–145.Google Scholar
- 5.Doan A., Madhavan J., Dhamankar R., Domingos P., and Halevy A.Y. Learning to match ontologies on the Semantic Web. VLDB J., 12(4):303–319, 2003.Google Scholar
- 6.Euzenat J. and Shvaiko P. Ontology Matching. Springer, 2007.Google Scholar
- 7.Gal A. Why is schema matching tough and what can we do about it? ACM SIGMOD Rec., 35(4):2–5, 2006.Google Scholar
- 8.Gal A., Anaby-Tavor A., Trombetta A., and Montesi D. A framework for modeling and evaluating automatic semantic reconciliation. VLDB J., 14(1):50–67, 2005.Google Scholar
- 9.Giunchiglia F., Marchese M., and Zaihrayeu I. Encoding classifications into lightweight ontologies. J. Data Semantics, 8:57–81, 2007.Google Scholar
- 10.Giunchiglia F. and Shvaiko P. Semantic Matching. Knowl. Eng. Rev., 18(3):265–280, 2003.Google Scholar
- 11.Giunchiglia F., Shvaiko P., and Yatskevich M. Discovering missing background knowledge in ontology matching. In Proc. 17th European Conf. on Artificial Intelligence, 2006, pp. 382–386.Google Scholar
- 12.Giunchiglia F., Yatskevich M., Avesani P., and Shvaiko P. A large scale dataset for the evaluation of ontology matching systems. Knowl. Eng. Rev., 23:1–22, 2008.Google Scholar
- 13.Giunchiglia F., Yatskevich M., and Shvaiko P. Semantic matching: algorithms and implementation. J. Data Semantics, 9:1–38, 2007.Google Scholar
- 15.Madhavan J., Bernstein P., and Rahm E. Generic schema matching with Cupid. In Proc. 27th Int. Conf. on Very Large Data Bases, 2001, pp. 48–58.Google Scholar
- 16.Noy N. and Musen M. The PROMPT suite: interactive tools for ontology merging and mapping. Int. J. Hum. Comput. Stud., 59(6):983–1024, 2003.Google Scholar
- 18.Shvaiko P. and Euzenat J. A survey of schema-based matching approaches. J. Data Semantics, 4:146–171, 2005.Google Scholar
- 19.Spaccapietra S. and Parent C. Conflicts and correspondence assertions in interoperable databases. ACM SIGMOD Rec., 20(4):49–54, 1991.Google Scholar