TY - GEN
T1 - Mining semi-structured online knowledge bases to answer natural language questions on community QA websites
AU - Sondhi, Parikshit
AU - Zhai, Cheng Xiang
N1 - Publisher Copyright:
Copyright 2014 ACM.
PY - 2014/11/3
Y1 - 2014/11/3
N2 - Over the past few years, community QA websites (e.g. Yahoo! Answers) have become a useful platform for users to post questions and obtain answers. However, not all questions posted there receive informative answers or are answered in a timely manner. In this paper, we show that the answers to some of these questions are available in online domain-specific knowledge bases and propose an approach to automatically discover those answers. In the proposed approach, we would first mine appropriate SQL query patterns by leveraging an existing collection of QA pairs, and then use the learned query patterns to answer previously unseen questions by returning relevant entities from the knowledge base. Evaluation on a collection of health domain questions from Yahoo! Answers shows that the proposed method is effective in discovering potential answers to user questions from an online medical knowledge base.
AB - Over the past few years, community QA websites (e.g. Yahoo! Answers) have become a useful platform for users to post questions and obtain answers. However, not all questions posted there receive informative answers or are answered in a timely manner. In this paper, we show that the answers to some of these questions are available in online domain-specific knowledge bases and propose an approach to automatically discover those answers. In the proposed approach, we would first mine appropriate SQL query patterns by leveraging an existing collection of QA pairs, and then use the learned query patterns to answer previously unseen questions by returning relevant entities from the knowledge base. Evaluation on a collection of health domain questions from Yahoo! Answers shows that the proposed method is effective in discovering potential answers to user questions from an online medical knowledge base.
UR - http://www.scopus.com/inward/record.url?scp=84937597358&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937597358&partnerID=8YFLogxK
U2 - 10.1145/2661829.2661968
DO - 10.1145/2661829.2661968
M3 - Conference contribution
AN - SCOPUS:84937597358
T3 - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
SP - 341
EP - 350
BT - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
Y2 - 3 November 2014 through 7 November 2014
ER -