Leveraging Incrementally Enriched Domain Knowledge to Enhance Service Categorization

  • Authors:

  • Patrick Hung;Jia Zhang;Jian Wang;Zheng Li;Neng Zhang;Keqing He

  • Affiliations:

  • University of Ontario Institute of Technology, Canada;Carnegie Mellon University, Silicon Valley, USA;State Key Lab of Software Engineering, Computer School, Wuhan University, China;State Key Lab of Software Engineering, Computer School, Wuhan University, China;State Key Lab of Software Engineering, Computer School, Wuhan University, China;State Key Lab of Software Engineering, Computer School, Wuhan University, China

  • Venue:
  • International Journal of Web Services Research
  • Year:
  • 2012

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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.