Relational term-suggestion graphs incorporating multipartite concept and expertise networks

  • Authors:
  • Jyh-Ren Shieh;Ching-Yung Lin;Shun-Xuan Wang;Ja-Ling Wu

  • Affiliations:
  • National Taiwan University, Taipei, Taiwan;IBM Thomas J. Watson Research Center, New York, USA;National Taiwan University;National Taiwan University

  • Venue:
  • 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
  • Year:
  • 2014

Quantified Score

Hi-index 0.00

Visualization

Abstract

Term suggestions recommend query terms to a user based on his initial query. Suggesting adequate terms is a challenging issue. Most existing commercial search engines suggest search terms based on the frequency of prior used terms that match the leading alphabets the user types. In this article, we present a novel mechanism to construct semantic term-relation graphs to suggest relevant search terms in the semantic level. We built term-relation graphs based on multipartite networks of existing social media, especially from Wikipedia. The multipartite linkage networks of contributor-term, term-category, and term-term are extracted from Wikipedia to eventually form term relation graphs. For fusing these multipartite linkage networks, we propose to incorporate the contributor-category networks to model the expertise of the contributors. Based on our experiments, this step has demonstrated clear enhancement on the accuracy of the inferred relatedness of the term-semantic graphs. Experiments on keyword-expanded search based on 200 TREC-5 ad-hoc topics showed obvious advantage of our algorithms over existing approaches.