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	Constrained information-theoretic tripartite graph clustering to identify semantically similar relations
	Wang, Chenguang ; Song, Yangqiu ; Roth, Dan ; Wang, Chi ; Han, Jiawei ; Ji, Heng ; Zhang, Ming
	2015
英文摘要	In knowledge bases or information extraction results, differently expressed relations can be semantically similar (e.g., (X, wrote, Y) and (X, 's written work, Y)). Therefore, grouping semantically similar relations into clusters would facilitate and improve many applications, including knowledge base completion, information extraction, information retrieval, and more. This paper formulates relation clustering as a constrained tripartite graph clustering problem, presents an efficient clustering algorithm and exhibits the advantage of the constrained framework. We introduce several ways that provide side information via must-link and cannot-link constraints to improve the clustering results. Different from traditional semi-supervised learning approaches, we propose to use the similarity of relation expressions and the knowledge of entity types to automatically construct the constraints for the algorithm. We show improved relation clustering results on two datasets extracted from human annotated knowledge base (i.e., Freebase) and open information extraction results (i.e., ReVerb data).; EI; 3882-3889; 2015-January
语种	英语
出处	24th International Joint Conference on Artificial Intelligence, IJCAI 2015
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436947]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wang, Chenguang,Song, Yangqiu,Roth, Dan,et al. Constrained information-theoretic tripartite graph clustering to identify semantically similar relations. 2015-01-01.

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