Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2013.040637
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 6, 2013.
Abstract: With the increasing number of published Web services providing similar functionalities, it’s very tedious for a service consumer to make decision to select the appropriate one according to her/his needs. 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. 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 and ranking web services using latent factors. In our experiment, we evaluated our Service Discovery and Ranking approach by calculating the precision ([email protected]) and normalized discounted cumulative gain (NDCGn).
Mustapha AZNAG, Mohamed QUAFAFOU and Zahi JARIR, “Correlated Topic Model for Web Services Ranking” International Journal of Advanced Computer Science and Applications(IJACSA), 4(6), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040637