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The clustering and sorting behavior of ants, as well as the foraging behavior of birds in nature represented sources of inspiration for designing clustering methods applicable in computer science. This paper investigates how biologically-inspired clustering methods can be adapted to cluster Semantic Web services aiming at the efficiency of the discovery process. The methods consider the semantic similarity between services as the main clustering criterion. To measure the semantic similarity between two services, we propose a matching method that evaluates the degree of match between the semantic description of the two services. We have tested the biologically-inspired clustering methods on the SAWSDL service retrieval test collection (SAWSDL-TC) benchmark, and we have comparatively evaluated their performance using the Dunn index and the Average-Item Cluster Similarity metric, the latter being introduced in this paper. Copyright © 2011 John Wiley & Sons, Ltd.