Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach

doi:10.1109/TBDATA.2018.2867583

	Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach
	Li, Manling; Wang, Yuanzhuo; Zhang, Denghui; Jia, Yantao; Cheng, Xueqi
	2022-06-01
发表期刊	IEEE TRANSACTIONS ON BIG DATA
ISSN	2332-7790
卷号	8 期号:3 页码:630-643
摘要	Link prediction over a knowledge graph aims to predict the missing head entities h or tail entities t and missing relations r for a triple (h, r, t). Recent years have witnessed great advance of knowledge graph embedding based link prediction methods, which represent entities and relations as elements of a continuous vector space. Most methods learn the embedding vectors by optimizing a margin-based loss function, where the margin is used to separate negative and positive triples in the loss function. The loss function utilizes the general structures of knowledge graphs, e.g., the vector of r is the translation of the vector of h and t, and the vector of t should be the nearest neighbor of the vector of h + r. However, there are many particular structures, and can be employed to promote the performance of link prediction. One typical structure in knowledge graphs is hierarchical structure, which existing methods have much unexplored. We argue that the hierarchical structures also contain rich inference patterns, and can further enhance the link prediction performance. In this paper, we propose a hierarchy-constrained link prediction method, called hTransM, on the basis of the translation-based knowledge graph embedding methods. It can adaptively determine the optimal margin by detecting the single-step and multi-step hierarchical structures. Moreover, we prove the effectiveness of hTransM theoretically, and experiments over three benchmark datasets and two sub-tasks of link prediction demonstrate the superiority of hTransM.
关键词	Link prediction knowledge graph embedding hierarchy
DOI	10.1109/TBDATA.2018.2867583
收录类别	SCI
语种	英语
资助项目	National Grand Fundamental Research 973 Program of China[2013CB329602] ; National Grand Fundamental Research 973 Program of China[2014CB340401] ; National Natural Science Foundation of China[61572469] ; National Natural Science Foundation of China[61402442] ; National Natural Science Foundation of China[61572473] ; National Natural Science Foundation of China[61303244] ; National Natural Science Foundation of China[61402022] ; National Natural Science Foundation of China[61572467] ; Beijing nova program ; Beijing Natural Science Foundation[4154086]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS记录号	WOS:000795107500005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19553
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Manling; Wang, Yuanzhuo
作者单位	Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Li, Manling,Wang, Yuanzhuo,Zhang, Denghui,et al. Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach[J]. IEEE TRANSACTIONS ON BIG DATA,2022,8(3):630-643.
APA	Li, Manling,Wang, Yuanzhuo,Zhang, Denghui,Jia, Yantao,&Cheng, Xueqi.(2022).Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach.IEEE TRANSACTIONS ON BIG DATA,8(3),630-643.
MLA	Li, Manling,et al."Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach".IEEE TRANSACTIONS ON BIG DATA 8.3(2022):630-643.