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|Title: ||Exploiting semantic association to answer ‘vague queries’.|
|Authors: ||Zhu, Jianhan|
|Editors: ||Li, Yuefeng|
|Keywords: ||Query expansion|
|Issue Date: ||2006|
|Publisher: ||IOS Press|
|Citation: ||ZHU, J., EISENSTADT, M., SONG, D. and DENHAM, C. 2006.
Exploiting semantic association to answer ‘vague queries’. In: Y.
LI, M. LOOI and N. ZHONG, eds. Advances in Intelligent IT – Active
Media Technology 2006: Volume 138 (Frontiers in Artificial
Intelligence and Applications). Fairfax, VA: IOS Press. pp. 73-78|
|Abstract: ||Although today’s 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 user’s 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 Amazon’s own book search fail.|
|Appears in Collections:||Book chapters (Computing)|
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