Particle Swarm Optimization for Clustering Semantic Web Services

This paper presents a method for Web service clustering based on Particle Swarm Optimization aiming at the efficiency of the discovery process. The proposed method clusters services based on the similarity between their semantic descriptions. To evaluate the semantic similarity we have defined a set of metrics which compute the degree of match between two services. The proposed metrics take into consideration the hierarchical and property-based non-hierarchical relations between the concepts that semantically describe the input and output service parameters. These metrics can be applied to the exact, subsume and sibling match. To test our method for service clustering we have used the SAWSDL-TC service collection. The performance of the clustering method has been evaluated using the Dunn Index, Intra-Cluster Variance and Average-Item Cluster Similarity metrics.