document: 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 . 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 .
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