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.