WTCluster: Utilizing Tags for Web Services Clustering

  • Liang Chen
  • Liukai Hu
  • Zibin Zheng
  • Jian Wu
  • Jianwei Yin
  • Ying Li
  • Shuiguang Deng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7084)


Clustering web services would greatly boost the ability of web service search engine to retrieve relevant ones. An important restriction of traditional studies on web service clustering is that researchers focused on utilizing web services’ WSDL (Web Service Description Language) documents only. The singleness of data source limits the accuracy of clustering. Recently, web service search engines such as Seekda! allow users to manually annotate web services using so called tags, which describe the function of the web service or provide additional contextual and semantical information. In this paper, we propose a novel approach called WTCluster, in which both WSDL documents and tags are utilized for web service clustering. Furthermore, we present and evaluate two tag recommendation strategies to improve the performance of WTCluster. The comprehensive experiments based on a dataset consists of 15,968 real web services demonstrate the effectiveness of WTCluster and tag recommendation strategies.


Service Discovery Query Term Content Vector WSDL Document Service Discovery Problem 
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.


  1. 1.
    Agarwal, S., Studer, R.: Automatic matchmaking of web services. In: International Conference on Web Services, pp. 45–54 (2006)Google Scholar
  2. 2.
    Al-Masri, E., Mahmoud, Q.H.: Investigating web services on the world wide web. In: International World Wide Web Conference, pp. 795–804 (2008)Google Scholar
  3. 3.
    Benatallah, B., Hacid, M., Leger, A., Rey, C., Toumani, F.: On automating web services discovery. The VLDB Journal 14(1), 84–96 (2005)CrossRefGoogle Scholar
  4. 4.
    Cilibrasi, R.L., Vitnyi, P.M.B.: The google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)CrossRefGoogle Scholar
  5. 5.
    Dong, X., Halevy, A., Madhavan, J., Nemes, E., Zhang, J.: Similarity search for web services. In: International Conference on Very Large Data Bases, pp. 372–383 (2004)Google Scholar
  6. 6.
    Elgazzar, K., Hassan, A.E., Martin, P.: Clustering wsdl documents to bootstrap the discovery of web services. In: International Conference on Web Services, pp. 147–154 (2009)Google Scholar
  7. 7.
    Hu, S., Muthusamy, V., Li, G., Jacobsen, H.A.: Distributed automatic service composition in large-scale systems. In: Proc. of Distributed Event-Based Systems Conference, pp. 233–244 (2008)Google Scholar
  8. 8.
    Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, New Jersey (1988)zbMATHGoogle Scholar
  9. 9.
    Church, K., Gale, W.: Inverse document frequency (idf): a measure of deviations from poisson. In: Proceedings of the ACL 3rd workshop on Very Large Corpora, pp. 121–130 (1995)Google Scholar
  10. 10.
    Klusch, M., Fries, B., Sycara, K.: Automated semantic web service discovery with owls-mx. In: International Conference on Autonomous Agents and Multiagent Systems, pp. 915–922 (2006)Google Scholar
  11. 11.
    Liu, F., Shi, Y., Yu, J., Wang, T., Wu, J.: Measuring similarity of web services based on wsdl. In: International Conference on Web Services, pp. 155–162 (2010)Google Scholar
  12. 12.
    Liu, W., Wong, W.: Web service clustering using text mining techniques. International Journal of Agent-Oriented Software Engineering 3(1), 6–26 (2009)CrossRefGoogle Scholar
  13. 13.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. of the Fifth Symposium on Math, Statistics, and Probability, pp. 281–297 (1967)Google Scholar
  14. 14.
    Nayak, R.: Data mining in web service discovery and monitoring. International Journal of Web Services Research 5(1), 62–80 (2008)CrossRefGoogle Scholar
  15. 15.
    Porter, M.F.: An algorithm for suffix stripping. Program. 14(3), 130–137 (1980)CrossRefGoogle Scholar
  16. 16.
    Lim, S.-Y., Song, M.-H., Lee, S.-J.: The Construction of Domain Ontology and its Application to Document Retrieval. In: Yakhno, T. (ed.) ADVIS 2004. LNCS, vol. 3261, pp. 117–127. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Zhang, Y., Zheng, Z., Lyu, M.R.: Wsexpress: A qos-aware search engine for web services. In: International Conference on Web Services, pp. 91–98 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Liang Chen
    • 1
  • Liukai Hu
    • 1
  • Zibin Zheng
    • 2
  • Jian Wu
    • 1
  • Jianwei Yin
    • 1
  • Ying Li
    • 1
  • Shuiguang Deng
    • 1
  1. 1.Zhejiang UniversityChina
  2. 2.The Chinese University of Hong KongChina

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