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Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping
Abstract
Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.
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