A Latent Semantic Indexing-Based Approach to Determine Similar Clusters in Large-scale Schema Matching
Schema matching plays a central role in identifying the semantic correspondences across shared-data applications, such as data integration. Due to the increasing size and the widespread use of XML schemas and different kinds of ontologies, it becomes toughly challenging to cope with large-scale schema matching. Clustering-based matching is a great step towards more significant reduction of the search space and thus improved efficiency. However, methods used to identify similar clusters depend on literally matching terms. To improve this situation, in this paper, a new approach is proposed which uses Latent Semantic Indexing that allows retrieving the conceptual meaning between clusters. The experimental evaluations show encourage results towards building efficient large-scale matching approaches.
KeywordsLarge-scale Schema Clustering-based matching Similar Clusters Latent semantic indexing
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- 4.Bonifati, A., Mecca, G., Pappalardo, A., Raunich, S., Summa, G.: Schema mapping verification: the spicy way. In: EDBT 2008, France,, pp. 85–96 (2008)Google Scholar
- 5.Deerwester, S., Dumais, S.T., Harshman, R.: Indexing by latent semantic analysis. Journal of American Society for Information Science 41, 391–407Google Scholar
- 9.Landauer, T.: Handbook of Latent Semantic Analysis (2007)Google Scholar
- 10.Peukert, E., Massmann, S., Konig, K.: Comparing similarity combination methods for schema matching. In: GI-Workshop, pp. 692–701 (2010)Google Scholar
- 11.Rahm, E.: Towards large-scale schema and ontology matching. In: Data-Centric Systems and Applications, vol. 5258, pp. 3–27. Springer (2011)Google Scholar