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Place Semantics into Context: Service Community Discovery from the WSDL Corpus

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7084)

Abstract

We propose a novel framework to automatically discover service communities that group together related services in a diverse and large scale service space. Community discovery is a key enabler to address a set of fundamental issues in service computing, which include service discovery, service composition, and quality-based service selection. The standard Web service description language, WSDL, primarily describes a service from the syntactic perspective and rarely provides rich service descriptions. This hinders the direct application of traditional document clustering approaches. In order to attack this central challenge, the proposed framework applies Non-negative Matrix Factorization (NMF) to the WSDL corpus for service community discovery. NMF has demonstrated its effectiveness in clustering high-dimensional sparse data while offering intuitive interpretability of the clustering result. NMF-based community discovery is further augmented via semantic extensions of the WSDL descriptions. The extended semantics are first computed based on the information sources outside the WSDL corpus. They are then seamlessly integrated with NMF, which makes the semantic extensions fit in the context of the original services. The experiments on real world Web services are presented to show the effectiveness of the proposed framework.

References

  1. 1.
    Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc, Boston (1999)Google Scholar
  2. 2.
    Bose, A., Nayak, R., Bruza, P.: Improving web service discovery by using semantic models. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 366–380. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Bouguettaya, A., Yu, Q., Liu, X., Malik, Z.: Service-centric framework for a digital government application. In: IEEE Transactions on Services Computing, vol. 99(PrePrints) (2010)Google Scholar
  4. 4.
    Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Trans. Knowl. Data Eng. 17(12), 1624–1637 (2005)CrossRefGoogle Scholar
  5. 5.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: KDD ’01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 269–274. ACM, New York (2001)CrossRefGoogle Scholar
  6. 6.
    Ding, C.H.Q., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: KDD, pp. 126–135 (2006)Google Scholar
  7. 7.
    Dong, X., Halevy, A., Madhavan, J., Nemes, E., Zhang, J.: Similarity search for web services. In: VLDB 2004: Proceedings of the Thirtieth International Conference on Very Large Data Bases, pp. 372–383, VLDB Endowment (2004)Google Scholar
  8. 8.
    Elgazzar, K., Hassan, A.E., Martin, P.: Clustering wsdl documents to bootstrap the discovery of web services. In: ICWS, pp. 147–154 (2010)Google Scholar
  9. 9.
    Klusch, M., Fries, B., Sycara, K.: Automated semantic web service discovery with owls-mx. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems AAMAS 2006, pp. 915–922. ACM Press, New York (2006)CrossRefGoogle Scholar
  10. 10.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)CrossRefGoogle Scholar
  11. 11.
    Liu, F., Shi, Y., Yu, J., Wang, T., Wu, J.: Measuring similarity of web services based on wsdl. In: ICWS, pp. 155–162 (2010)Google Scholar
  12. 12.
    Liu, W., Wong, W.: Discovering homogenous service communities through web service clustering. In: Kowalczyk, R., Huhns, M.N., Klusch, M., Maamar, Z., Vo, Q.B. (eds.) SOCASE 2008. LNCS, vol. 5006, pp. 69–82. Springer, Heidelberg (2008)Google Scholar
  13. 13.
    Liu, X., Huang, G., Mei, H.: Discovering homogeneous web service community in the user-centric web environment. IEEE T. Services Computing 2(2), 167–181 (2009)CrossRefGoogle Scholar
  14. 14.
    Lovasz, L.: Matching Theory (North-Holland Mathematics Studies). Elsevier Science Ltd. (1986)Google Scholar
  15. 15.
    Ma, J., Zhang, Y., He, J.: Efficiently finding web services using a clustering semantic approach. In: CSSSIA 2008: Proceedings of the 2008 International Workshop on Context Enabled Source and Service Selection, Integration and Adaptation, pp. 1–8. ACM, New York (2008)Google Scholar
  16. 16.
    Sahami, M., Heilman, T.D.: A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 377–386. ACM, New York (2006)Google Scholar
  17. 17.
    Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR 2003, pp. 267–273. ACM, New York (2003)CrossRefGoogle Scholar
  18. 18.
    Yu, Q., Bouguettaya, A.: Framework for web service query algebra and optimization. TWEB 2(1) (2008)Google Scholar
  19. 19.
    Yu, Q., Liu, X., Bouguettaya, A., Medjahed, B.: Deploying and managing web services: issues, solutions, and directions. VLDB Journal 17(3), 537–572 (2008)CrossRefGoogle Scholar
  20. 20.
    Yu, Q., Rege, M.: On service community learning: A co-clustering approach. In: ICWS, pp. 283–290 (2010)Google Scholar
  21. 21.
    Yu, T., Zhang, Y., Lin, K.-J.: Efficient algorithms for web services selection with end-to-end qos constraints. ACM Trans. Web 1(1), 6 (2007)CrossRefGoogle Scholar
  22. 22.
    Zeng, L., Benatallah, B., Ngu, A., Dumas, M., Kalagnanam, J., Chang, H.: Qos-aware middleware for web services composition. IEEE Trans. Softw. Eng. 30(5), 311–327 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Qi Yu
    • 1
  1. 1.College of Computing and Information ScienceRochester Institute of TechnologyUSA

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