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WT-LDA: User Tagging Augmented LDA for Web Service Clustering

  • Liang Chen
  • Yilun Wang
  • Qi Yu
  • Zibin Zheng
  • Jian Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8274)

Abstract

Clustering Web services that groups together services with similar functionalities helps improve both the accuracy and efficiency of the Web service search engines. An important limitation of existing Web service clustering approaches is that they solely focus on utilizing WSDL (Web Service Description Language) documents. There has been a recent trend of using user-contributed tagging data to improve the performance of service clustering. Nonetheless, these approaches fail to completely leverage the information carried by the tagging data and hence only trivially improve the clustering performance. In this paper, we propose a novel approach that seamlessly integrates tagging data and WSDL documents through augmented Latent Dirichlet Allocation (LDA). We also develop three strategies to preprocess tagging data before being integrated into the LDA framework for clustering. Comprehensive experiments based on real data and the implementation of a Web service search engine demonstrate the effectiveness of the proposed LDA-based service clustering approach.

Keywords

Latent Dirichlet Allocation Service Discovery Topic Distribution Service Cluster Word Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Liang Chen
    • 1
  • Yilun Wang
    • 1
  • Qi Yu
    • 2
  • Zibin Zheng
    • 3
  • Jian Wu
    • 1
  1. 1.Zhejiang UniversityChina
  2. 2.Rochester Institute of TechnologyUSA
  3. 3.The Chinese University of Hong KongHong Kong

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