Probabilistic Topic Models for Web Services Clustering and Discovery

Abstract

In Information Retrieval the Probabilistic Topic Models were originally developed and utilized for topic extraction and document modeling. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent factors from web service descriptions. These extracted latent factors are then used to group the services into clusters. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering web services using latent factors. In our experiment, we compared the accuracy of the three probabilistic clustering algorithms (PLSA, LDA and CTM) with that of a classical clustering algorithm. We evaluated also our service discovery approach by calculating the precision (P@n) and normalized discounted cumulative gain (NDCGn). The results show that both approaches based on CTM and LDA perform better than other search methods.