Collaborative querying for enhanced information retrieval

Lin Fu*, Dion Hoe Lian Goh, Schubert Shou Boon Foo, Yohan Supangat

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Citations (Scopus)

Abstract

Communication and collaboration with other people is a major theme in the information seeking process. Collaborative querying addresses this issue by sharing other users' search experiences to help users formulate appropriate queries to a search engine. This paper describes a collaborative querying system that helps users with query formulation by finding previously submitted similar queries through mining web logs. The system operates by clustering and recommending related queries to users using a hybrid query similarity identification approach. The system employs a graph-based approach to visualize the query recommendations.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsRachel Heery, Liz Lyon
PublisherSpringer Verlag
Pages378-388
Number of pages11
ISBN (Print)3540230130, 9783540230137
DOIs
Publication statusPublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3232
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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