Evaluation of two heuristic approaches to solve the ontology meta-matching problem |
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Authors: | Jorge Martinez-Gil José F Aldana-Montes |
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Affiliation: | 1.Department of Computer Languages and Computing Sciences,University of Málaga,Malaga,Spain |
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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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