Using a genetic algorithm for optimizing the similarity aggregation step in the process of ontology alignment

This paper addresses the increasingly encountered challenge of ontology alignment. Starting with basic similarity measures such as the syntactic similarity, represented by the Levenshtein or Jaro Distance, semantic similarities, which make use of WordNet and taxonomy similarities, our new system uses a genetic algorithm specially designed for the task of optimizing the aggregation of these measures. Assessment done by us in the last part of the paper demonstrates the usefulness of the genetic algorithm, which manage a consistent improvement of classical alignment methods.