Date of Publication :7th August 2016
Abstract: Discovering appropriate services quickly for dynamic service composition is a challenging issue. Clustering technique partitions the available services into clusters of similar services. During discovery of matched services for a query, semantic matching of service capabilities is performed only to a particular cluster which is most relevant to the query and other clusters are ignored as irrelevant. Thus clustering improves the performance of semantic discovery by eliminating irrelevancy. In one of our previous research work, two similarity models, one for computing similarity between services(called Output Similarity Model) while clustering them and the other(called Total Similarity Model) for finding matched services for a given query using clusters along with selection of similarity threshold and recommendation of complete linkage criterion for computing inter-cluster distance are proposed for service discovery using hierarchical agglomerative clustering. As an extension of our previous work, in this paper, an experimental evaluation has been performed to analyze the performance of OSM in regard to effective removal of irrelevancy and the strength of prioritizing parameters during discovery. Further, the clustering solutions obtained using Output Similarity Model are compared with those produced by standard methods such as syntactic similarity and Word Net similarity based methods. Though clustering improves the performance of discovery by eliminating irrelevant clusters, still is required to employ semantic matching to the services present in the relevant cluster. This involves invoking semantic reasoning during querying. To resolve this limitation, after clustering, an indexing technique is suggested to the resulting clustering solution. With this model, the invoking of semantic reasoning is completely eliminated.
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