MatchBench: Benchmarking schema matching algorithms for schematic correspondences

Chenjuan Guo, Cornelia Hedeler, Norman W. Paton, Alvaro A A Fernandes

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    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. © 2013 Springer-Verlag.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    Place of PublicationBerlin
    PublisherSpringer Nature
    Number of pages14
    ISBN (Print)9783642394669
    Publication statusPublished - 2013
    Event29th British National Conference on Databases, BNCOD 2013 - Oxford
    Duration: 1 Jul 2013 → …

    Publication series



    Conference29th British National Conference on Databases, BNCOD 2013
    Period1/07/13 → …


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