%0 Journal Article %1 IJACSA.2013.040637 %A Mustapha AZNAG Mohamed QUAFAFOU, Zahi JARIR %D 2013 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K Data Discovery, Learning, Machine Models,Web Ranking, Representation, Topic service %N 6 %T Correlated Topic Model for Web Services Ranking %U http://ijacsa.thesai.org/ %V 4 %X 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 (P@n) and normalized discounted cumulative gain (NDCGn).

@article{IJACSA.2013.040637, 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 (P@n) and normalized discounted cumulative gain (NDCGn).}, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Mustapha AZNAG Mohamed QUAFAFOU}, Zahi JARIR}, biburl = {https://www.bibsonomy.org/bibtex/254887b52b18317b21d9a8b493311ef4f/thesaiorg}, interhash = {d681428272df8f9e10e87ca4128e3ba6}, intrahash = {54887b52b18317b21d9a8b493311ef4f}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {Data Discovery, Learning, Machine Models,Web Ranking, Representation, Topic service}, number = 6, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Correlated Topic Model for Web Services Ranking}}, url = {http://ijacsa.thesai.org/}, volume = 4, year = 2013 }