Abstract
This paper presents an ant-inspired method for clustering semantic Web services. The method considers the degree of semantic similarity between services as the main clustering criterion. To measure the semantic similarity between two services we propose a matching method and a set of metrics. The proposed metrics evaluate the degree of match between the ontology concepts describing two services. We have tested the ant-inspired clustering method on the SAWSDL-TC benchmark and we have evaluated its performance using the Dunn Index, the Intra-Cluster Variance metric and an original metric we introduce in this paper.
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Pop, C.B., Chifu, V.R., Salomie, I., Dinsoreanu, M., David, T., Acretoaie, V. (2010). Semantic Web Service Clustering for Efficient Discovery Using an Ant-Based Method. In: Essaaidi, M., Malgeri, M., Badica, C. (eds) Intelligent Distributed Computing IV. Studies in Computational Intelligence, vol 315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15211-5_3
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DOI: https://doi.org/10.1007/978-3-642-15211-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15210-8
Online ISBN: 978-3-642-15211-5
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