Abstract
Schema matching is the problem of finding relationships among concepts across heterogeneous data sources (heterogeneous in format and in structure). Starting from the “hidden meaning” associated to schema labels (i.e. class/attribute names) it is possible to discover relationships among the elements of different schemata. Lexical annotation (i.e. annotation w.r.t. a thesaurus/lexical resource) helps in associating a “meaning” to schema labels. However, accuracy of semi-automatic lexical annotation methods on real-world schemata suffers from the abundance of non-dictionary words such as compound nouns and word abbreviations. In this work, we address this problem by proposing a method to perform schema labels normalization which increases the number of comparable labels. Unlike other solutions, the method semi-automatically expands abbreviations and annotates compound terms, without a minimal manual effort. We empirically prove that our normalization method helps in the identification of similarities among schema elements of different data sources, thus improving schema matching accuracy.
Acknowledgements: This work was partially supported by MUR FIRB Network Peer for Business project (http://www.dbgroup.unimo.it/nep4b) and by the IST FP6 STREP project 2006 STASIS (http://www.dbgroup.unimo.it/stasis).
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Sorrentino, S., Bergamaschi, S., Gawinecki, M., Po, L. (2009). Schema Normalization for Improving Schema Matching. In: Laender, A.H.F., Castano, S., Dayal, U., Casati, F., de Oliveira, J.P.M. (eds) Conceptual Modeling - ER 2009. ER 2009. Lecture Notes in Computer Science, vol 5829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04840-1_22
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