Optimizing Ontology Alignments by using NSGA-II

In this paper, we propose a novel approach based on NSGA-II to address the problem of optimizing the aggregation of three different basic similarity measures (syntactic measure, linguistic measure and taxonomy-based measure) and get a single similarity metric. Comparing with conventional genetic algorithm, the proposed method is able to realize three goals simultaneously, i.e., maximizing the alignment recall, the alignment precision and the F-measure and find the optimal solutions which could avoid bias to recall or precision value. Experiment results show that the proposed approach is effective.