Ontology Matching aims at finding correspondences between two different ontologies with overlapping parts in order to bring them into a mutual agreement. The set of correspondences, called alignment, is obtained by computing an aggregated similarity value for all pairs of ontology entities through a weighted approach. Unfortunately, the similarity aggregation task is a very complex optimization process, above all, when no information is known about ontology characteristics. This work presents a hybrid approach which aims at efficiently optimizing the weights for the similarity aggregation task without knowing a priori the ontology features. The effectiveness of our approach is shown by aligning ontologies belonging to the well-known OAEI benchmark dataset and by executing a comparison based on the Wilcoxon's signed rank test which highlights that our proposal statistically outperforms both its genetic counterpart and a traditional no evolutionary approach.
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