Ontology alignment optimization method based on NSGA-II

Abstract

In order to solve the problem of optimizing the mapping result integration of three distinct similarity measurement technologies (grammar based similarity measurement, linguistic based similarity measurement and taxonomy based similarity measurement), a method based on NSGA-II is proposed. Compared with the traditional genetic algorithm, this method can achieve the maximum mapping recall ratio, precision ratio and f-measure value at the same time. The ontology mapping results obtained by this method can solve the problem of preference for recall or precision. The experimental results indicate that the put forward method is feasible.