126
4
archive
disk0/00/00/01/26
2009-04-06 19:12:29
2009-04-07 14:02:51
2009-04-06 19:12:29
conference_item
show
0
-
Shieh
Jyh-Ren
National Taiwan University
-
Hsieh
Yung-Huan
National Taiwan University
-
Yeh
Yang-Ting
National Taiwan University
-
Chung Su
Tse
National Taiwan University
-
Lin
Ching-Yung
IBM T. J. Watson Research Center
-
Wu
Ja-Ling
National Taiwan University
Building Term Suggestion Relational Graphs from Collective Intelligence
pub
public
poster
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.
2009-04
1091-1091
18th International World Wide Web Conference
Madrid, Spain
April 20th-24th, 2009
conference
TRUE
126
4
126
1
application/pdf
en
public
p1091.pdf
published
p1091.pdf
549261
http://www2009.eprints.org/126/1/p1091.pdf