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Evaluation of two heuristic approaches to solve the ontology meta-matching problem

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Abstract

Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.

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Authors and Affiliations

  1. Department of Computer Languages and Computing Sciences, University of Málaga, Boulevard Luis Pasteur s/n, 29071, Malaga, Spain

    Jorge Martinez-Gil & José F. Aldana-Montes

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  1. Jorge Martinez-Gil
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  2. José F. Aldana-Montes
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Correspondence to José F. Aldana-Montes.

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Martinez-Gil, J., Aldana-Montes, J.F. Evaluation of two heuristic approaches to solve the ontology meta-matching problem. Knowl Inf Syst 26, 225–247 (2011). https://doi.org/10.1007/s10115-009-0277-0

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  • DOI: https://doi.org/10.1007/s10115-009-0277-0

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