Abstract
The elaboration of semantic matching between hetero geneous data sources is a fundamental step in the design of data sharing applications. This task is tedious and often error prone if handled manually. Therefore, many systems have been developed for its automation. But, the majority of them focus on the problem of finding simple (one-to-one) matching. This is likely due to the fact that complex (many-to-many) matching raises a far more difficult problem since the search space of concept combinations can be tremendously large. This article presents Indigo, a system which can compute complex matching by taking into account data sources’ context. First, it enriches data sources with complex concepts extracted from their respective development artifacts. It then computes a mapping between the two data sources thus enhanced.
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References
Rahm, E., Bernstein, P.A.: A Survey of Approaches to Automatic Schema Matching. VLDB Journal 10(4), 334–350 (2001)
Bououlid, I.Y., Vachon, J.: Context Analysis for Semantic Mapping of Data Sources Using a Multi-Strategy Machine Learning Approach. In: Proc. of the International Conf. on Enterprise Information Systems (ICEIS 2005), Miami, pp. 445–448 (2005)
Bououlid, I.Y., Vachon, J.: A Context-Based Approach for Linguistic Matching. In: Proc. of the International Conf. on Software and Data Technologies (ICSOFT 2007), Barcelona, Spain (2007)
Li, W.S., Clifton, C.: Semantic Integration in Heterogeneous Databases Using Neural Networks. In: Proc. of the 20th Conf. on Very Large Databases (VLDB), pp. 1–12 (1994)
Euzenat, J., et al.: State of the Art on Ontology Alignment. Part of a research project funded by the IST Program of the Commission of the European Communities, project number IST-2004-507482. Knowledge Web Consortium (2004)
Xu, L., Embley, D.: Using domain ontologies to discover direct and indirect matches for schema elements. In: Proc. of the Semantic Integration Workshop (2003)
He, B., Chang, K.C.-C., Han, J.: Discovering complex matchings across web query interfaces: A correlation mining approach. In: Proc. of the SIGKDD conf. (2004)
Dhamankar, R., Lee, Y., Doan, A., Halevy, A., Domingos, P.: iMAP: Discovering Complex Semantic Matches between Database Schemas. In: Proc. of the ACM SIGMOD Conference on Management of Data, pp. 383–394. ACM Press, New York (2004)
Sun Microsystems (2005), http://java.sun.com/developer/releases/petstore/
Adobe (2007), http://www.adobe.com/devnet/blueprint/
DotNetGuru.org (2003), http://www.dotnetguru.org/modules.php
McUmber, R.: Developing pet store using rup and xde. Web Site (2003)
Cohen, W., Hirsh, H.: Joins that Generalize: Text Classification using Whirl. In: Proc. of the Fourth Int. Conf. on Knowledge Discovery and Data Mining (1998)
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Bououlid Idrissi, Y., Vachon, J. (2007). A Context-Based Approach for the Discovery of Complex Matches Between Database Sources. In: Wagner, R., Revell, N., Pernul, G. (eds) Database and Expert Systems Applications. DEXA 2007. Lecture Notes in Computer Science, vol 4653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74469-6_84
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DOI: https://doi.org/10.1007/978-3-540-74469-6_84
Publisher Name: Springer, Berlin, Heidelberg
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