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Service Selection in Business Service Ecosystem

  • Sujoy Basu
  • Sven Graupner
  • Kivanc Ozonat
  • Sharad Singhal
  • Donald Young
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5472)

Abstract

A world-wide community of service providers has a presence on the web, and people seeking services typically go to the web as an initial place to search for them. Service selection is comprised of two steps: finding service candidates using search engines and selecting those which meet desired service properties best. Within the context of Web Services, the service selection problem has been solved through common description frameworks that make use of ontologies and service registries. However, the majority of service providers on the web does not use such frameworks and rather make service descriptions available on their web sites that provide human targeted content.

This paper addresses the service selection problem under the assumption that a common service description framework does not exist, and services have to be selected using the more unstructured information available on the web.

The approach described in this paper has the following steps. Search engines are employed to find service candidates from dense requirement formulations extracted from user input. Text classification techniques are used to identify services and service properties from web content retrieved from search links. Service candidates are then ranked based on how well they support desired properties. Initial experiments have been conducted to validate the approach.

Keywords

Service selection unstructured data service discovery service matchmaking text analysis service ontology service description framework 

References

  1. 1.
    Krafzig, D., Banke, K., Slama, D.: Enterprise SOA: Service Oriented Architecture Best Practices. Prentice-Hall, Englewood Cliffs (2005)Google Scholar
  2. 2.
    Universal Description, Discovery and Integration (UDDI), http://uddi.xml.org
  3. 3.
  4. 4.
    Lapata, M., Keller, F.: Web-based Models for Natural Language Processing. ACM Transactions of Speech and Language Processing 2(1), 1–30 (2005), http://homepages.inf.ed.ac.uk/mlap/Papers/tslp05.pdf CrossRefGoogle Scholar
  5. 5.
    Powerset. Discover Facts. Unlock Meaning. Scan Summaries, http://www.powerset.com
  6. 6.
    W3C Semantic Web, http://www.w3.org/2001/sw
  7. 7.
    Chakrabarti, S.: Mining the Web: Analysis of Hypertext and Semi Structured Data. Morgan Kaufmann, San Francisco (2002)Google Scholar
  8. 8.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  9. 9.
    Nasraoui, O., Zaïane, O.R., Spiliopoulou, M., Mobasher, B., Masand, B., Yu, P.S. (eds.) WebKDD 2005. LNCS (LNAI), vol. 4198. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    WordNet. Cognitive Science Laboratory. Princeton University, http://wordnet.princeton.edu
  11. 11.
    Dumais, S.T., Furnas, G.W., Landauer, T.K., Deerwester, S.: Using latent semantic analysis to improve information retrieval. In: Proceedings of CHI 1988: Conference on Human Factors in Computing, pp. 281–285. ACM, New York (1988)Google Scholar
  12. 12.
    Hoovers. Online business registry, http://www.hoovers.com
  13. 13.
    Dun and Bradstreet. Provider of international and US business credit information and credit reports, http://www.dnb.com
  14. 14.
    ThomasNet. Provider of information about Industrial Manufacturers, http://www.thomasnet.com
  15. 15.
    Forman, G.: An Extensive Empirical Study of Feature Selection Metrics for Text Classification. Journal of Machine Learning Research 3, 1289–1305 (2003)zbMATHGoogle Scholar
  16. 16.
    RosettaNet: Trading Partner Implementation Requirements (TPIR) Partner Interface Process (PIP) Maintenance Service, http://www.rosettanet.org/shop/store/catalogs/publications.html
  17. 17.
    Deerwester, S., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. Journal of the Society for Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  18. 18.
    Yahoo! Search BOSS (Build your Own Search Service), http://developer.yahoo.com/search/boss/
  19. 19.
    W3C, Web Services Description Language (WSDL) 1.1, http://www.w3.org/TR/wsdl
  20. 20.
    Dong, X., Halevy, A., Madhavan, J., Nemes, E., Zhang, J.: Similarity search for web services. In: Proceedings of VLDB 2004, pp. 372–383 (2004)Google Scholar
  21. 21.
    Ma, J., Zhang, Y., He, J.: Efficiently finding web services using a clustering semantic approach. In: Proceedings of WWW 2008 (2008)Google Scholar
  22. 22.
    Wang, Y., Stroulia, E.: Flexible Interface Matching for Web-Service Discovery. In: Proceedings of the Fourth international Conference on Web information Systems Engineering (2003)Google Scholar
  23. 23.
  24. 24.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sujoy Basu
    • 1
  • Sven Graupner
    • 1
  • Kivanc Ozonat
    • 1
  • Sharad Singhal
    • 1
  • Donald Young
    • 1
  1. 1.Hewlett-Packard LaboratoriesPalo AltoUSA

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