Aggregated Similarity Optimization in Ontology Alignment through Multiobjective Particle Swarm Optimization

Abstract: The basic idea behind the ontology is to conceptualize information that is published in electronic format. The problem of ontology alignment is defined as identifying the relationship shared by the set of different entities where each entity belongs to separate ontology. The amount of similarity between two entities from two different ontologies takes part into the ontology alignment process. There are several similarity measuring methods available in the existing literature for measuring the similarity between two discrete entities from different ontologies. To obtain a comprehensive and precise result, all the similarity measures are integrated. One of the ways to combine the various similarity measures is weight-based similarity aggregation. Usually the weights with respect to various similarity measures are assigned manually or through some method. But most of the existing techniques suffer from lack of optimality. Also many evolutionary based approaches are available to find the optimal solution for weight-based similarity aggregation but they are designed as single objective optimization problem. This fact has inspired us to develop a multiobjective particle swarm based optimization algorithm for generating optimal weight based similarity aggregation to get a optimal alignment. In this article, two objectives precision and recall are simultaneously optimized. Moreover a local search is conducted for replacing the worst population in the new generation by best population acquired from the history. The proposed study is evaluated using an artificial data set and performance of the proposed method is compared with that of its single objective versions.  

Keywords: ontology alignment, particle swarm optimization, multiobjective optimization, f-score.