Building Term Suggestion Relational Graphs from Collective Intelligence

Presented at: 18th International World Wide Web Conference (WWW2009)

by Jyh-Ren Shieh, Yung-Huan Hsieh, Yang-Ting Yeh, Tse Chung Su, Ching-Yung Lin, Ja-Ling Wu

Webpage: http://www2009.eprints.org/126/1/p1091.pdf

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.

Keywords: Poster Session


Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2009/paper/126


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