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Leveraging Incrementally Enriched Domain Knowledge to Enhance Service Categorization

Author

Listed:
  • Jia Zhang

    (Carnegie Mellon University, Silicon Valley, USA)

  • Jian Wang

    (State Key Lab of Software Engineering, Computer School, Wuhan University, China)

  • Patrick Hung

    (University of Ontario Institute of Technology, Canada)

  • Zheng Li

    (State Key Lab of Software Engineering, Computer School, Wuhan University, China)

  • Neng Zhang

    (State Key Lab of Software Engineering, Computer School, Wuhan University, China)

  • Keqing He

    (State Key Lab of Software Engineering, Computer School, Wuhan University, China)

Abstract

This paper reports the authors’ study over an open service and mashup repository, ProgrammableWeb, which groups stored services into predefined categories. Leveraging such a unique structural feature and hidden domain knowledge of the service repository, they extend the Support Vector Machine (SVM)-based text classification technique to enhance service-oriented categorization. An iterative approach is presented to automatically verify and adjust service categorization, which will incrementally enrich domain ontology and in turn enhance the accuracy of service categorization.

Suggested Citation

  • Jia Zhang & Jian Wang & Patrick Hung & Zheng Li & Neng Zhang & Keqing He, 2012. "Leveraging Incrementally Enriched Domain Knowledge to Enhance Service Categorization," International Journal of Web Services Research (IJWSR), IGI Global, vol. 9(3), pages 43-66, July.
    • Handle: RePEc:igg:jwsr00:v:9:y:2012:i:3:p:43-66
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