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EvoMatch: An Evolutionary Algorithm for Inferring Schematic Correspondences

  • Chenjuan Guo
  • Cornelia Hedeler
  • Norman W. Paton
  • Alvaro A. A. Fernandes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8320)

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.

Keywords

Schema Match Equivalent Attribute Evolutionary Search Schematic Correspondence Input Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chenjuan Guo
    • 1
  • Cornelia Hedeler
    • 1
  • Norman W. Paton
    • 1
  • Alvaro A. A. Fernandes
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterUK

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