A Joint Model of Entity Linking and Predicate Recognition for Knowledge Base Question Answering

Yang Li, Qingliang Miao, ChenXin Yin, Chao Huo, Wenxiang Mao, Changjian Hu, Feiyu Xu
2018 China Conference on Knowledge Graph and Semantic Computing  
In the paper, we build a QA system which can automatically find the right answers from Chinese knowledge base. In particular, we first identify all possible topic entities in the knowledge base for a question. Then some predicate scores are utilized to pre-rank all candidate triple paths of topic entities by logistic model. Second, we use a joint training entity linking and predicate recognition model to re-rank candidate triple paths for the question. Finally, the paper selects the answer
more » ... nent from matched triple path based on heuristic rules. Our approach achieved the averaged F1-score of 57.67% on test data which obtained the second place in the contest of CCKS 2018 COQA task.
dblp:conf/ccks/LiMYHMHX18 fatcat:qia6vzjiqzg2rh6md7zfhl5i6a