Multilabel Learning for Automatic Web Services Tagging

Abstract

Recently, some web services portals and search engines as Biocatalogue and Seekda!, have allowed users to manually annotate Web services using tags. User Tags provide meaningful descriptions of services and allow users to index and organize their contents. Tagging technique is widely used to annotate objects in Web 2.0 applications. In this paper we propose a novel probabilistic topic model (which extends the CorrLDA model - Correspondence Latent Dirichlet Allocation-) to automatically tag web services according to existing manual tags. Our probabilistic topic model is a latent variable model that exploits local correlation labels. Indeed, exploiting label correlations is a challenging and crucial problem especially in multi-label learning context. Moreover, several existing systems can recommend tags for web services based on existing manual tags. In most cases, the manual tags have better quality. We also develop three strategies to automatically recommend the best tags for web services. We also propose, in this paper, WS-Portal; An Enriched Web Services Search Engine which contains 7063 providers, 115 sub-classes of category and 22236 web services crawled from the Internet. In WS-Portal, severals technologies are employed to improve the effectiveness of web service discovery (i.e. web services clustering, tags recommendation, services rating and monitoring). Our experiments are performed out based on real-world web services. The comparisons of Precision@n, Normalised Discounted Cumulative Gain (NDCGn) values for our approach indicate that the method presented in this paper outperforms the method based on the CorrLDA in terms of ranking and quality of generated tags.