Abstract
Schema matching provides an important foundation for both manual and semi-automatic derivation of mappings between sources. However, schema matchers typically return large numbers of potentially inconsistent matches that are neither conducive to automatic mapping generation nor readily digested by mapping developers. This paper presents a method, EvoMatch, for automatically inferring schematic correspondences, from which mappings can be generated directly. It aims to offer a more expressive characterization of the relationships between sources than matches identified by existing schema matching methods. In particular, the paper contributes: i) an evolutionary search method for inferring schematic correspondences; ii) an objective function for calculating the fitness value of a solution within the search space; and iii) an empirical evaluation assessing the effectiveness of EvoMatch for inferring schematic correspondences in comparison with well established existing techniques. In doing so, EvoMatch automatically identifies correspondences that can be used directly to bootstrap information integration systems, or to inform the manual refinement of mappings.
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Guo, C., Hedeler, C., Paton, N.W., Fernandes, A.A.A. (2013). EvoMatch: An Evolutionary Algorithm for Inferring Schematic Correspondences. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XII. Lecture Notes in Computer Science, vol 8320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45315-1_1
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