Knowledge and Information Systems

, Volume 26, Issue 2, pp 225–247 | Cite as

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

Regular Paper

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.

Keywords

Ontology meta-matching Knowledge management Information integration 

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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Department of Computer Languages and Computing SciencesUniversity of MálagaMalagaSpain

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