Building term suggestion relational graphs from collective intelligence

Authors:
Jyh-Ren Shieh;Yung-Huan Hsieh;Yang-Ting Yeh;Tse-Chung Su;Ching-Yung Lin;Ja-Ling Wu
Affiliations:
National Taiwan University, Taipei, Taiwan, ROC , Taiwan Roc;National Taiwan University, Taipei, Taiwan, ROC , Taiwan Roc;National Taiwan University, Taipei, Taiwan, ROC , Taiwan Roc;National Taiwan University, Taipei, Taiwan, ROC , Taiwan Roc;IBM T. J. Watson Research Center, Hawthorne, New York, USA, NY, USA;National Taiwan University, Taipei, Taiwan, ROC , Taiwan Roc
Venue:
Proceedings of the 18th international conference on World wide web
Year:
2009

Citing 0
Cited 3

A spatio-temporal framework for related topic search in micro-blogging

AMT'10 Proceedings of the 6th international conference on Active media technology
Building Multi-Modal Relational Graphs for Multimedia Retrieval

International Journal of Multimedia Data Engineering & Management
Relational term-suggestion graphs incorporating multipartite concept and expertise networks

ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.