中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach

文献类型：期刊论文

作者Li, Manling; Wang, Yuanzhuo; Zhang, Denghui; Jia, Yantao; Cheng, Xueqi
刊名IEEE TRANSACTIONS ON BIG DATA
出版日期2022-06-01
卷号8期号:3页码:630-643
关键词Link prediction knowledge graph embedding hierarchy
ISSN号2332-7790
DOI10.1109/TBDATA.2018.2867583
英文摘要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.
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
语种英语
WOS记录号WOS:000795107500005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/19552]  
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。