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CONQUER: A Methodology for Context-Aware Query Processing on the World Wide Web


  • Veda C. Storey

    () (Computer Information Systems Department, J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia 30302-4015)

  • Andrew Burton-Jones

    () (Management Information Systems Division, Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, British Columbia V6T 1Z2, Canada)

  • Vijayan Sugumaran

    () (Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, Michigan 48309)

  • Sandeep Purao

    () (College of Information Sciences and Technology, Pennsylvania State University, University Park, State College, Pennsylvania 16802)


A major impediment to accurate information retrieval from the World Wide Web is the inability of search engines to incorporate semantics in the search process. This research presents a methodology, CONQUER (CONtext-aware QUERy processing), that enhances the semantic content of Web queries using two complementary knowledge sources: lexicons and ontologies. The methodology constructs a semantic net using the original query as a seed, and refines the net with terms from the two knowledge sources. The enhanced query, represented by the refined semantic net, can be executed by search engines. This paper describes the methodology and its implementation in a prototype. An empirical evaluation shows that queries suggested by the prototype produce more relevant results than those obtained by the original queries. The research, thus, provides a successful demonstration of the use of existing knowledge sources to enhance the semantic content of Web queries. The paper concludes by identifying potential uses of such enhancements of search technology in organizational contexts.

Suggested Citation

  • Veda C. Storey & Andrew Burton-Jones & Vijayan Sugumaran & Sandeep Purao, 2008. "CONQUER: A Methodology for Context-Aware Query Processing on the World Wide Web," Information Systems Research, INFORMS, vol. 19(1), pages 3-25, March.
  • Handle: RePEc:inm:orisre:v:19:y:2008:i:1:p:3-25

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    Cited by:

    1. Stefan Feuerriegel & Nicolas Prollochs, 2018. "Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation," Papers 1805.03308,


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