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2009
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Building term suggestion relational graphs from collective intelligence

10 years 4 months ago
Building term suggestion relational graphs from collective intelligence
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. ...
Jyh-Ren Shieh, Yung-Huan Hsieh, Yang-Ting Yeh, Tse
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where WWW
Authors Jyh-Ren Shieh, Yung-Huan Hsieh, Yang-Ting Yeh, Tse-Chung Su, Ching-Yung Lin, Ja-Ling Wu
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