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Exploiting semantic association to answer 'vague queries'.

Zhu, Jianhan; Eisenstadt, Marc; Song, Dawei; Denham, Chris

Authors

Jianhan Zhu

Marc Eisenstadt

Dawei Song

Chris Denham



Contributors

Yuefeng Li
Editor

Mark Looi
Editor

Ning Zhong
Editor

Abstract

Although todays web search engines are very powerful, they still fail to provide intuitively relevant results for many types of queries, especially ones that are vaguely-formed in the users own mind. We argue that associations between terms in a search query can reveal the underlying information needs in the users mind and should be taken into account in search. We propose a multi-faceted approach to detect and exploit such associations. The CORDER method measures the association strength between query terms, and queries consisting of terms having low association strength with each other are seen as ˜vague queries. For a vague query, we use WordNet to find related terms of the query terms to compose extended queries, relying especially on the role of least common subsumers (LCS). We use relation strength between terms calculated by the CORDER method to refine these extended queries. Finally, we use the Hyperspace Analogue to Language (HAL) model and information flow (IF) method to expand these refined queries. Our initial experimental results on a corpus of 500 books from Amazon shows that our approach can find the right books for users given authentic vague queries, even in those cases where Google and Amazons own book search fail.

Start Date Jun 7, 2006
Publication Date May 31, 2006
Publisher IOS Press
Pages 73-78
Series Title Frontiers in artificial intelligence and applications
Series Number 138
ISBN 9781586036157
Institution Citation ZHU, J., EISENSTADT, M., SONG, D. and DENHAM, C. 2006. Exploiting semantic association to answer 'vague queries'. In Li, Y., Looi, M. and Zhong, N. (eds.) Advances in intelligent IT: proceedings of the 4th International conference on active media technology (AMT06), 7-9 June 2006, Brisbane, Australia. Frontiers in artificial intelligence and applications, 138. Amsterdam: IOS Press [online], pages 73-78. Available from: http://ebooks.iospress.nl/volumearticle/2511
Keywords Query expansion; Similarity; Association strength; Semantic space
Publisher URL http://ebooks.iospress.nl/volumearticle/2511

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