Leveraging Incrementally Enriched Domain Knowledge to Enhance Service Categorization

Jia Zhang, Jian Wang, Patrick Hung, Zheng Li, Neng Zhang and Keqing He
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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

International Journal of Web Services Research (IJWSR), 2012, vol. 9, issue 3, 43-66

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.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:9:y:2012:i:3:p:43-66

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