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Discovering Web Services Based on Probabilistic Latent Factor Model

  • Yanchun Zhang
  • Jiangang Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4505)

Abstract

Recently, web services have been increasingly used to integrate and build business applications on the Internet. Once a web service is published and deployed, clients and other applications can discover and invoke it. With the incredibly increasing number of Web services on the Internet, it is critical for service users to discover desired services that match their requirements. In this paper, we present a novel approach for discovering web services. Based on the current dominating mechanisms of the discovering and describing web services with UDDI and WSDL, the proposed method utilizes Probabilistic Latent Semantic Analysis (PLSA) to capture semantic concepts hidden behind words in a query and the advertisements in services so that services matching is expected to be carried out at concept level. We also present related algorithms and preliminary experiments to evaluate the effectiveness of our approach.

Keywords

Web services web services discovering 

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References

  1. 1.
    Deerwester, S., Dumais, S.T.: Indexing by Latent Semantic Analysis. Journal American Society for Information Retrieval, 391–407 (1990)Google Scholar
  2. 2.
    Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T.: The Vocabulary Problem in Human-System Communication. Communication of ACM 30(11), 964–971 (1987)CrossRefGoogle Scholar
  3. 3.
    Garofalakis, J., Panagis, Y., Sakkopoulo, E., Tsakalidis, A.: Web Service Discovery Mechanisms: Looking for a Needle in a Haystack? In: International Workshop on Web Engineering, August 10 (2004)Google Scholar
  4. 4.
    Hao, Y., Zhang, Y.: Web Services Discovery based on Schema Matching. In: Proceedings of the 30th Australiasian Computer Science Conference (ACSC 2007), Australia (Feb. 2007)Google Scholar
  5. 5.
    Hofmann, T.: Probabilistic Latent Semantic Analysis. In: Proceedings of the 22nd Annual ACM Conference on Research and Development in Information Retrieval, Berkeley, California, August 1999, pp. 50–57. ACM Press, New York (1999)CrossRefGoogle Scholar
  6. 6.
    Hofmann, T.: Probabilistic Latent Semantic Indexing. In: Proceedings of the 22nd Annual International SIGIR Conference on Research and Development in Information Retrieval (1999)Google Scholar
  7. 7.
    Hofmann, T.: Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning 42(1-2), 177–196 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Hull, R., Benedikt, M., Christophides, V., Su, J.: E-services: A look behind the curtain. In: Proceedings of the Twenty-Second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (June 2003)Google Scholar
  9. 9.
    Klein, M., Bernstein, A.: Toward High-Precision Service Retrieval. IEEE Internet Computing 8(1), 30–36 (2004)CrossRefGoogle Scholar
  10. 10.
    Larkey, L.S.: Automatic essay grading using text classification techniques. In: Proceedings of ACM SIGIR (1998)Google Scholar
  11. 11.
    Ma, J., Cao, J., Zhang, Y.: A Probabilistic Semantic Approach for Discovering Web Services. To appear in the 16th International World Wide Web Conference(WWW2007), Banff, Alberta, Canada, May 8 -12 (2007)Google Scholar
  12. 12.
    Oussani, M., Bouguettaya, A.: Efficient Access to Web Services. IEEE Internet Computing 8(2), 34–44 (2004)CrossRefGoogle Scholar
  13. 13.
    Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.P.: Semantic Matching of Web Services Capabilities. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, p. 333. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Sivashanmugam, K., Verma, K., Sheth, A., Miller, J.: Adding Semantics to Web Services Standards. In: Proceedings of the International Conference on Web Services, ICWS’03, pp. 395–401 (2003)Google Scholar
  15. 15.
    Staab, S., Van der Aalst, W., Benjamins, V.R., Sheth, A., Miller, J.A., Bussler, C., Maedche, A., Fensel, D., Gannon, D.: Web services: been there, done that? IEEE Intelligent Systems 18(1), 72–85 (2003)CrossRefGoogle Scholar
  16. 16.
    Sajjanhar, A., Hou, J., Zhang, Y.: Algorithm for Web Services Matching. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 665–670. Springer, Heidelberg (2004)Google Scholar
  17. 17.
    UDDI Version 2.03 Data Structure Reference UDDI Committee Specification (19 July 2002), http://uddi.org/pubs/DataStructure-V2.03-Published-20020719.htm
  18. 18.
    Wang, Y., Stroulia, E.: Semantic Structure Matching for Assessing Web-Service Similarity. In: Orlowska, M.E., Weerawarana, S., Papazoglou, M.P., Yang, J. (eds.) ICSOC 2003. LNCS, vol. 2910, pp. 194–207. Springer, Heidelberg (2003)Google Scholar
  19. 19.
    Xu, G., Zhang, Y., Ma, J., Zhou, X.: Discovering User Access Pattern Based on Probabilistic Latent Factor Model. In: Proceedings of the 16th Australasian Database Conference, vol. 39, Newcastle, Australia, pp. 27–35 (2005)Google Scholar
  20. 20.
  21. 21.
    Yang, Y., Pedersen, J.: A Comparative Study on Feature Selection in Text Categorization. In: International Conference on Machine Learning (1997)Google Scholar
  22. 22.
    Zaremski, A.M., Wing, J.M.: Signature Matching: a Tool for Using Software Libraries. ACM Transactions on Software Engineering and Methodology 4(2), 146–170 (1995)CrossRefGoogle Scholar
  23. 23.
  24. 24.

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Yanchun Zhang
    • 1
  • Jiangang Ma
    • 1
  1. 1.School of Computer Science & Mathematics, Victoria UniversityAustralia

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