Semantics-based automated service discovery

Aabhas V. Paliwal, Basit Shafiq, Jaideep Vaidya, Hui Xiong, Nabil Adam

Research output: Contribution to journalArticlepeer-review

112 Scopus citations

Abstract

A vast majority of web services exist without explicit associated semantic descriptions. As a result many services that are relevant to a specific user service request may not be considered during service discovery. In this paper, we address the issue of web service discovery given nonexplicit service description semantics that match a specific service request. Our approach to semantic-based web service discovery involves semantic-based service categorization and semantic enhancement of the service request. We propose a solution for achieving functional level service categorization based on an ontology framework. Additionally, we utilize clustering for accurately classifying the web services based on service functionality. The semantic-based categorization is performed offline at the universal description discovery and integration (UDDI). The semantic enhancement of the service request achieves a better matching with relevant services. The service request enhancement involves expansion of additional terms (retrieved from ontology) that are deemed relevant for the requested functionality. An efficient matching of the enhanced service request with the retrieved service descriptions is achieved utilizing Latent Semantic Indexing (LSI). Our experimental results validate the effectiveness and feasibility of the proposed approach.

Original languageEnglish (US)
Article number5744076
Pages (from-to)260-275
Number of pages16
JournalIEEE Transactions on Services Computing
Volume5
Issue number2
DOIs
StatePublished - 2012

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management

Keywords

  • Web services publishing
  • services discovery process and methodology
  • web services discovery

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