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Building term suggestion relational graphs from collective intelligence

Published: 20 April 2009 Publication History

Abstract

This paper proposes an effective approach to provide relevant search terms for conceptual Web search. 'Semantic Term Suggestion' function has been included so that users can find the most appropriate query term to what they really need. Conventional approaches for term suggestion involve extracting frequently occurring key terms from retrieved documents. They must deal with term extraction difficulties and interference from irrelevant documents. In this paper, we propose a semantic term suggestion function called Collective Intelligence based Term Suggestion (CITS). CITS provides a novel social-network based framework for relevant terms suggestion with a semantic graph of the search term without limiting to the specific query term. A visualization of semantic graph is presented to the users to help browsing search results from related terms in the semantic graph. The search results are ranked each time according to their relevance to the related terms in the entire query session. Comparing to two popular commercial search engines, a user study of 18 users on 50 search terms showed better user satisfactions and indicated the potential usefulness of proposed method in real-world search applications.

References

[1]
Wikipedia, http://en.wikipedia.org/.
[2]
C. Fellbaum, editor, "WordNet: An Electronic Lexical Database," The MIT Press, Cambridge, MA, 1998.
[3]
Google, http://www.google.com/.
[4]
Yahoo!, http://www.yahoo.com/.

Cited By

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  • (2014)Relational term-suggestion graphs incorporating multipartite concept and expertise networksACM Transactions on Intelligent Systems and Technology10.1145/2542182.25422015:1(1-25)Online publication date: 3-Jan-2014
  • (2013)Building Multi-Modal Relational Graphs for Multimedia RetrievalMultimedia Data Engineering Applications and Processing10.4018/978-1-4666-2940-0.ch009(171-192)Online publication date: 2013
  • (2011)Building Multi-Modal Relational Graphs for Multimedia RetrievalInternational Journal of Multimedia Data Engineering and Management10.4018/jmdem.20110401022:2(19-41)Online publication date: Apr-2011
  • Show More Cited By

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cover image ACM Conferences
WWW '09: Proceedings of the 18th international conference on World wide web
April 2009
1280 pages
ISBN:9781605584874
DOI:10.1145/1526709

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2009

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Author Tags

  1. keyword expansion
  2. re-ranking
  3. social network

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2014)Relational term-suggestion graphs incorporating multipartite concept and expertise networksACM Transactions on Intelligent Systems and Technology10.1145/2542182.25422015:1(1-25)Online publication date: 3-Jan-2014
  • (2013)Building Multi-Modal Relational Graphs for Multimedia RetrievalMultimedia Data Engineering Applications and Processing10.4018/978-1-4666-2940-0.ch009(171-192)Online publication date: 2013
  • (2011)Building Multi-Modal Relational Graphs for Multimedia RetrievalInternational Journal of Multimedia Data Engineering and Management10.4018/jmdem.20110401022:2(19-41)Online publication date: Apr-2011
  • (2010)Incorporating Multi-partite Networks and Expertise to Construct Related-Term GraphsProceedings of the 2010 IEEE International Conference on Data Mining Workshops10.1109/ICDMW.2010.89(535-542)Online publication date: 13-Dec-2010
  • (2010)A Spatio-temporal Framework for Related Topic Search in Micro-BloggingActive Media Technology10.1007/978-3-642-15470-6_8(63-73)Online publication date: 2010

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