Ranking Community Answers via Analogical Reasoning

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

by Xudong Tu, Xin-Jing Wang, Dan Feng, Lei Zhang

Webpage: http://www2009.eprints.org/194/1/p1227.pdf

Due to the lexical gap between questions and answers, automatically detecting right answers becomes very challenging for community question-answering sites. In this paper, we propose an analogical reasoning-based method. It treats questions and answers as relational data and ranks an answer by measuring the analogy of its link to a query with the links embedded in previous relevant knowledge; the answer that links in the most analogous way to the new question is assumed to be the best answer. We based our experiments on 29.8 million Yahoo!Answer questionanswer threads and showed the effectiveness of the approach.

Keywords: Poster Session


Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2009/paper/194


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