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2017 IEEE International Conference on Web Services (ICWS) (2017)
Honolulu, Hawaii, USA
June 25, 2017 to June 30, 2017
ISBN: 978-1-5386-0752-7
pp: 9-16
Due to the rapid growth in both the number and diversity of Web services on the web, it becomes increasingly difficult for us to find the desired and appropriate Web services nowadays. Clustering Web services according to their functionalities becomes an efficient way to facilitate the Web services discovery as well as the services management. Existing methods for Web services clustering mostly focus on utilizing directly key features from WSDL documents, e.g., input/output parameters and keywords from description text. Probabilistic topic model Latent Dirichlet Allocation (LDA) is also adopted, which extracts latent topic features of WSDL documents to represent Web services, to improve the accuracy of Web services clustering. However, the power of the basic LDA model for clustering is limited to some extent. Some auxiliary features can be exploited to enhance the ability of LDA. Since the word vectors obtained by Word2vec is with higher quality than those obtained by LDA model, we propose, in this paper, an augmented LDA model (named WE-LDA) which leverages the high-quality word vectors to improve the performance of Web services clustering. In WE-LDA, the word vectors obtained by Word2vec are clustered into word clusters by K-means++ algorithm and these word clusters are incorporated to semi-supervise the LDA training process, which can elicit better distributed representations of Web services. A comprehensive experiment is conducted to validate the performance of the proposed method based on a ground truth dataset crawled from ProgrammableWeb. Compared with the state-of-the-art, our approach has an average improvement of 5.3% of the clustering accuracy with various metrics.
Feature extraction, Clustering algorithms, Semantics, Probabilistic logic, Mashups, Tools

M. Shi, J. Liu, D. Zhou, M. Tang and B. Cao, "WE-LDA: A Word Embeddings Augmented LDA Model for Web Services Clustering," 2017 IEEE International Conference on Web Services (ICWS), Honolulu, Hawaii, USA, 2017, pp. 9-16.
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