Abstract
Schema matching algorithms aim to identify relationships between database schemas, which are useful in many data integration tasks. However, the results of most matching algorithms are expressed as semantically inexpressive, 1-to-1 associations between pairs of attributes or entities, rather than semantically-rich characterisations of relationships. This paper presents a benchmark for evaluating schema matching algorithms in terms of their semantic expressiveness. The definition of such semantics is based on the classification of schematic heterogeneities of Kim et al.. The benchmark explores the extent to which matching algorithms are effective at diagnosing schematic heterogeneities. The paper contributes: (i) a wide range of scenarios that are designed to systematically cover several reconcilable types of schematic heterogeneities; (ii) a collection of experiments over the scenarios that can be used to investigate the effectiveness of different matching algorithms; and (iii) an application of the experiments for the evaluation of matchers from three well-known and publicly available schema matching systems, namely COMA++, Similarity Flooding and Harmony.
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Guo, C., Hedeler, C., Paton, N.W., Fernandes, A.A.A. (2013). MatchBench: Benchmarking Schema Matching Algorithms for Schematic Correspondences. In: Gottlob, G., Grasso, G., Olteanu, D., Schallhart, C. (eds) Big Data. BNCOD 2013. Lecture Notes in Computer Science, vol 7968. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39467-6_11
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DOI: https://doi.org/10.1007/978-3-642-39467-6_11
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