A FML-based fuzzy tuning for a memetic ontology alignment system

Ontology alignment systems are software tools aimed at producing a set of correspondences, called alignment, between two heterogeneous ontologies in order to bring them in a mutual agreement. Performing this task is an essential step to allow the exchange of information between people, organizations and web applications using ontologies for representing their view of the world. Currently, in spite of several ontology alignment systems have been developed, there is no a robust solution that seems capable of producing alignments with the same high quality on different alignment task instances. Mainly, this weakness of ontology alignment systems is due to the dependence of their behavior on a set of specific instance parameters. This work proposes to improve performance of a well-known memetic algorithm based ontology alignment system by adaptively regulating its specific instance parameters through a FML-based fuzzy tuning. The validity of our proposal is shown by aligning ontologies belonging to two well-known OAEI datasets and by performing a Wilcoxon's signed rank test which highlights that our proposal statistically outperforms its not fuzzy adaptive counterpart.