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