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Topology and Topic-Aware Service Clustering

Topology and Topic-Aware Service Clustering

Weifeng Pan, Jilei Dong, Kun Liu, Jing Wang
Copyright: © 2018 |Volume: 15 |Issue: 3 |Pages: 20
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781522542469|DOI: 10.4018/IJWSR.2018070102
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MLA

Pan, Weifeng, et al. "Topology and Topic-Aware Service Clustering." IJWSR vol.15, no.3 2018: pp.18-37. http://doi.org/10.4018/IJWSR.2018070102

APA

Pan, W., Dong, J., Liu, K., & Wang, J. (2018). Topology and Topic-Aware Service Clustering. International Journal of Web Services Research (IJWSR), 15(3), 18-37. http://doi.org/10.4018/IJWSR.2018070102

Chicago

Pan, Weifeng, et al. "Topology and Topic-Aware Service Clustering," International Journal of Web Services Research (IJWSR) 15, no.3: 18-37. http://doi.org/10.4018/IJWSR.2018070102

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Abstract

This article describes how the number of services and their types being so numerous makes accurately discovering desired services become a problem. Service clustering is an effective way to facilitate service discovery. However, the existing approaches are usually designed for a single type of service documents, neglecting to fully use the topic and topological information in service profiles and usage histories. To avoid these limitations, this article presents a novel service clustering approach. It adopts a bipartite network to describe the topological structure of service usage histories and uses a SimRank algorithm to measure the topological similarity of services; It applies Latent Dirichlet Allocation to extract topics from service profiles and further quantifies the topic similarity of services; It quantifies the similarity of services by integrating topological and topic similarities; It uses the Chameleon clustering algorithm to cluster the services. The empirical evaluation on real-world data set highlights the benefits provided by the combination of topological and topic similarities.

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