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).
Highlights
Web services1 [25] are defined as a software systems designed to support interoperable machine-to-machine interaction over a network
Before applying the proposed Web Service Discovery and Ranking, we deal the Web Services Description Language (WSDL) corpus. The objective of this preprocessing is to identify the functional terms of services, which describe the semantics of their functionalities
We evaluated the effectiveness of our Web Service Discovery and Ranking for the three probabilistic topic models using both methods Conditional www.ijacsa.thesai.org
Summary
Web services1 [25] are defined as a software systems designed to support interoperable machine-to-machine interaction over a network They are loosely coupled reusable software components that encapsulate discrete functionality and are distributed and programmatically accessible over the Internet. Mechanisms and techniques are required to help consumers to discover which one is better In this case one of the major filters adopted to evaluate these services is using Quality of Service (QoS) as a criterion. To address the limitation of keywords-based queries, we represent web service description as a vector and introduce a new approach for discovering and ranking web services based on probabilistic topic models. The probabilistic topic models are a way to deal with large volumes of data by discovering their hidden thematic structure. The probabilistic topic models use their hidden variables to discover the latent semantic structure in large textual data.
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