Abstract
Schema matching is a critical step in data integration from multiple heterogeneous data sources. This paper presents a new approach to schema matching, based on two observations. First, it is easier to find attribute correspondences between those schemas that are contextually similar. Second, the attribute correspondences found between these schemas can be used to help find new attribute correspondences between other schemas. Motivated by these observations, we propose a novel clustering-based approach to schema matching. First, we cluster schemas on the basis of their contextual similarity. Second, we cluster attributes of the schemas that are in the same schema cluster to find attribute correspondences between these schemas. Third, we cluster attributes across different schema clusters using statistical information gleaned from the existing attribute clusters to find attribute correspondences between more schemas. We leverage a fast clustering algorithm, the K-Means algorithm, to the above three clustering tasks. We have evaluated our approach in the context of integrating information from multiple web interfaces and the results show the effectiveness of our approach.
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References
Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: ICDE 2002, Washington, DC, USA, pp. 117–128. IEEE Computer Society Press, Los Alamitos (2002)
He, B., Chang, K., Han, J.: Discovering complex matchings across web query interfaces: a correlation mining approach. In: KDD 2004, pp. 148–157. ACM Press, New York (2004)
Wu, W., Yu, C., Doan, A., Meng, W.: An interactive clustering-based approach to integrating source query interfaces on the deep web. In: SIGMOD 2004, pp. 95–106. ACM Press, New York (2004)
He, B., Chang, K.: Statistical schema matching across web query interfaces. In: SIGMOD 2003, pp. 217–228. ACM Press, New York (2003)
Do, H., Rahm, E.: Coma - a system for flexible combination of schema matching approaches. In: VLDB 2002, HongKong (2002)
Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB jounal 10, 334–350 (2001)
He, H., Meng, W., Yu, C.T., Wu, Z.: Wise-integrator: An automatic integrator of web search interfaces for ecommerce. In: VLDB 2003, pp. 357–268 (2003)
Madhavan, J., Bernstein, P., Doan, A., Halevy, A.: Corpus-based schema matching. In: ICDE 2005 (2005)
Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, Chichester (1990)
Larsen, B., Aone, C.: Fast and effective text mining using linear-time document clustering. In: KDD 1999, pp. 16–22. ACM Press, New York (1999)
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McCraw-Hill, New York (1983)
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining (2000)
Lange, T., Roth, V., Braun, M.L., Buhmann, J.: Stability-based validation of clustering solutions. Neural Computation 16, 1299–1323 (2004)
Levine, E.E.: Resampling method for unsupervised estimation of cluster validity. Neural Computation 13, 2573–2593 (2001)
UIUC: Icq datasets: http://metaquerier.cs.uiuc.edu/repository/datasets/icq/index.html
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Pei, J., Hong, J., Bell, D. (2006). A Novel Clustering-Based Approach to Schema Matching. In: Yakhno, T., Neuhold, E.J. (eds) Advances in Information Systems. ADVIS 2006. Lecture Notes in Computer Science, vol 4243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11890393_7
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DOI: https://doi.org/10.1007/11890393_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46291-0
Online ISBN: 978-3-540-46292-7
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