Automatically Composing Services by Mining Process Knowledge from the Web

  • Bipin Upadhyaya
  • Ying Zou
  • Shaohua Wang
  • Joanna Ng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8274)


Current approaches in Service-Oriented Architecture (SOA) are challenging for users to get involved in the service composition due to the in-depth knowledge required for SOA standards and techniques. To shield users from the complexity of SOA standards, we automatically generate composed services for end-users using process knowledge available in the Web. Our approach uses natural language processing techniques to extract tasks. Our approach automatically identifies services required to accomplish the tasks. We represent the extracted tasks in a task model to find the services and then generate a user interface (UI) for a user to perform the tasks. Our case study shows that our approach can extract the tasks from how-to instructions Web pages with high precision (i.e., 90%). The generated task model helps to discover services and compose the found services to perform a task. Our case study shows that our approach can reach more than 90% accuracy in service composition by identifying accurate data flow relation between services.


task model service composition Web instructions UI generation 


  1. 1.
    Gerede, C.E., Hull, R., Ibarra, O.H., Su, J.: Automated composition of e-services: lookaheads. In: ICSOC, pp. 252–262 (2004)Google Scholar
  2. 2.
    Thomas, J.P., Thomas, M., Ghinea, G.: Modeling of web services flow. In: IEEE International Conference on E-Commerce, CEC 2003 (2003)Google Scholar
  3. 3.
    Upadhyaya, B., Khomh, F., Zou, Y., Lau, A., Ng, J.: A concept analysis approach for guiding users in service discovery. In: 2012 5th IEEE International Conference on SOCA, pp. 1–8 (2012)Google Scholar
  4. 4.
    Upadhyaya, B., Zou, Y., Xiao, H., Ng, J., Lau, A.: Migration of SOAP-based services to RESTful services. In: Proc. of the 13th IEEE International Symposium on WSE, pp. 105–114 (2011)Google Scholar
  5. 5.
    Miller, G.A.: WordNet: A Lexical Database for English. Communications of the ACM 38(11), 39–41Google Scholar
  6. 6.
    Li, L., Chou, W.: Automatic Message Flow Analyses for Web Services Based on WSDL. In: IEEE International Conference on Web Services (2007)Google Scholar
  7. 7.
    Hwang, S.Y., Lim, E.P., Lee, C.H., Chen, C.H.: Dynamic Web Service Selection for Reliable Web Service Composition. IEEE Transactions on Services Computing 1(2), 104–116 (2008)CrossRefGoogle Scholar
  8. 8.
    Bias, R.G., Mayhew, D.J.: Cost-Justifying usability. Morgan Kaufmann Publishers, San FranciscoGoogle Scholar
  9. 9.
  10. 10.
    Kassoff, M., Kato, D., Mohsin, W.: Creating GUIs for web services. IEEE Internet Comp. 7(5), 66–73Google Scholar
  11. 11.
    Mehandjiev, N., Namoune, A., Wajid, U., Macaulay, L., Sutcliffe, A.: End User Service Composition: Perceptions and Requirements. In: IEEE 8th European Conference on ECOWS, pp. 139–146 (2010)Google Scholar
  12. 12.
    Spillner, J., Feldmann, M., Braun, I., Springer, T., Schill, A.: Ad Hoc Usage of Web Services with Dynvoker. In: Mähönen, P., Pohl, K., Priol, T. (eds.) ServiceWave 2008. LNCS, vol. 5377, pp. 208–219. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Perkowitz, M., Philipose, M., Fishkin, K.P., Patterson, D.J.: Mining models of human activities from the web. In: WWW 2004, pp. 573–582 (2004)Google Scholar
  14. 14.
    Raphael, B., Bhatnagar, G., Smith, I.F.: Creation of flexible graphical user interfaces through model composition. Artif. Intell. Eng. Des. Anal. Manuf. 16(3), 173–184 (2002)CrossRefGoogle Scholar
  15. 15.
    Poibeau, T., Kosseim, L.: Proper Name Extraction from Non-Journalistic Texts. In: Proc. Computational Linguistics in the Netherlands (2001)Google Scholar
  16. 16.
    Xiao, H., Upadhyaya, B., Khomh, F., Zou, Y., Ng, J., Lau, A.: An automatic approach for extracting process knowledge from the Web. In: Proc. of ICWS, pp. 315–322 (2011)Google Scholar
  17. 17.
    Web Service Definition Language (WSDL),
  18. 18.
    Web Application Description Language,
  19. 19.
    World Wide Web Consortium (W3C),
  20. 20.
    Fielding, R.T., Taylor, R.N.: Principled design of the modern Web architecture, pp. 407–416Google Scholar
  21. 21.
    Richardson, L., Ruby, S.: RESTful web services (2007)Google Scholar
  22. 22.
    Ján, K., Necaský, M., Bartoš, T.: Generating XForms from an XML Schema. NDT (2), 706–714 (2010)Google Scholar
  23. 23.
    Turney, P.D.: Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 491–502. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  24. 24.
    IBM WebSphere Integration Developer,
  25. 25.
  26. 26.
  27. 27.
    wikihow-how to do anything,
  28. 28.
    eHow | Discover the expert in you,
  29. 29.
    Klein, D., Manning, C.D.: Accurate Unlexicalized Parsing. In: 41st Annual Meeting of the Association for Computational Linguistics, pp. 423–430 (2003)Google Scholar
  30. 30.
    Concur Task Trees (CTT),
  31. 31.
    Singh, P., Lin, T., Mueller, E.T., Lim, G., Perkins, T., Zhu, W.L.: Open Mind Common Sense: Knowledge acquisition from the general public. In: Meersman, R., Tari, Z. (eds.) CoopIS/DOA/ODBASE 2002. LNCS, vol. 2519, pp. 1223–1237. Springer, Heidelberg (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bipin Upadhyaya
    • 1
  • Ying Zou
    • 1
  • Shaohua Wang
    • 1
  • Joanna Ng
    • 2
  1. 1.Queen’s UniversityKingstonCanada
  2. 2.CAS ResearchIBM Canada Software LaboratoryMarkhamCanada

Personalised recommendations