Abstract
Various similarity measures have been proposed for ontology integration to identify and suggest possible matches of components in a semi-automatic process. A (basic) Multi Match Algorithm (MMA) can be used to combine these measures effectively, thus making it easier for users in such applications to identify “ideal” matches found. We propose a multi-level extension of MMA, called MLMA, which assumes the collection of similarity measures are partitioned by the user, and that there is a partial order on the partitions, also defined by the user. We have developed a running prototype of the proposed multi level method and illustrate how our method yields improved match results compared to the basic MMA. While our objective in this study has been to develop tools and techniques to support the hybrid approach we introduced earlier for ontology integration, the ideas can be applied in information sharing and ontology integration applications.
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Alasoud, A., Haarslev, V., Shiri, N.: A hybrid approach for ontology integration. In: Proc. VLDB Workshop on Ontologies-based techniques for DataBases and Information Systems (ODBIS), September 2-3, 2005, Trondheim, Norway (2005)
Artale, A., Franconi, E., Mandreoli, F.: Description logics for modeling dynamic information. In: Logics for Emerging Applications of Databases, Springer, Heidelberg (2003)
Baader, F., Celanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)
Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Learning to map between ontologies on the semantic web. In: Proc. 11th Int’l WWW Conference, Hawaii, US (2002)
Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Ontology matching: A machine learning approach. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies in Information Systems, pp. 397–416. Springer, Heildelberg (DE) (2003)
Do, H.H., Melnik, S., Rahm, E.: Comparison of schema matching evaluations. In: Proc. workshop on Web and Databases (2002)
Euzenat, J., Valtchev, P.: Similarity-based ontology alignment in OWL-Lite. In: Proc. 16th European Conference on Artificial Intelligence (ECAI 2004), Valencia, Spain (2004)
Giunchiglia, F., Shvaiko, P.: Semantic matching. In: Proc. IJCAI Workshop on ontologies and distributed systems, pp. 139–146 (2003)
Gu, J.: Multispace search for satisfiability and NP-hard problems. DIMACS Series in Discrete Mathematics and Theoretical Computer Science 35, 407–517 (1997)
Hu, W., Jian, N.S., Qu, Y.Z., Wang, Y.B.: GMO: A Graph Matching for Ontologies. In: Proc. K-Cap Workshop on Integrating Ontologies, pp. 43–50 (2005)
Hu, W., Cheng, G., Zheng, D., Zhong, X., Qu, Y.: The results of Falcon-AO. In: Proc. International workshop on Ontology Matching (OM), Athens, Georgia, U.S.A, November 5, 2006 (2006)
Kalfoglou, Y., Hu, B.: CROSI Mapping System (CMS). In: Proc. Integrating Ontologies Workshop, October 2, 2005, Banff, Canada (2005)
Li, W., Clifton, C.: SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks. IEEE Trans. on Data & Knowledge Engineering 33(1), 49–84 (2000)
Li, Y., Li, J., Zhang, D., Tang, J.: Results of ontology alignment with RiMOM. In: Proc. International workshop on Ontology Matching (OM), November 5, 2006, Athens, Georgia, U.S.A (2006)
Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with cupid. In: Proc. 27th VLDB Conference (2001)
Massmann, S., Engmann, D., Rahm, E., Tang, J.: Results of ontology alignment with COMA++. In: Proc. International workshop on Ontology Matching (OM), Athens, November 5, 2006, Georgia, U.S.A (2006)
Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: 18th Int. Conference on Data Engineering (ICDE), San Jose, California (2002)
Mitra, P., Noy, N.F., Jaiswal, A.R.: OMEN: A probabilistic ontology mapping tool. In: Proc. Workshop on Meaning Coordination and Negotiation, Hisroshima, Japan (2004)
Noy, N.F., Musen, M.A.: The PROMPT suite: Interactive tools for ontology merging and mapping. Journal of Human-Computer Studies 59(6), 983–1024 (2003)
Noy, N.F., Musen, M.A.: Anchor-PROMPT: Using non-local context for semantic matching. In: Proc. Workshop on Ontologies and Information Sharing (in conjunction with IJCAI), Seattle, WA (2001)
Rasmussen, E.: Clustering Algorithms. In: Frakes, W.B., Baeza–Yates, R. (eds.) Information Retrieval: Data Structures & Algorithms, Prentice Hall, Englewood Cliffs (1992)
Zhang, Z., Che, H.Y., Shi, P.F., Sun, Y., Gu, J.: An algebraic framework for schema matching. In: Fan, W., Wu, Z., Yang, J. (eds.) WAIM 2005. LNCS, vol. 3739, Springer, Heidelberg (2005)
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Alasoud, A., Haarslev, V., Shiri, N. (2007). A Multi-level Matching Algorithm for Combining Similarity Measures in Ontology Integration. In: Collard, M. (eds) Ontologies-Based Databases and Information Systems. ODBIS ODBIS 2006 2005. Lecture Notes in Computer Science, vol 4623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75474-9_1
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DOI: https://doi.org/10.1007/978-3-540-75474-9_1
Publisher Name: Springer, Berlin, Heidelberg
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