Knowledge Commons of Institute of Automation,CAS
Enhancing knowledge graph embedding with relational constraints | |
Li, Mingda; Sun, Zhengya; Zhang, Siheng; Zhang, Wensheng1,2 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
2021-03-14 | |
卷号 | 429页码:77-88 |
摘要 | Knowledge graph embedding is studied to embed entities and relations of a knowledge graph into continuous vector spaces, which benefits a variety of real-world applications. Among existing solutions, translational models, which employ geometric translation to design score function, have drawn much attention. However, these models primarily concentrate on evidence from observing whether the triplets are plausible, and ignore the fact that the relation also implies certain semantic constraints on its subject or object entity. In this paper, we present a general framework for enhancing knowledge graph embedding with relational constraints (KRC). Specifically, we elaborately design the score function by encoding regularities between a relation and its arguments into the translational embedding space. Additionally, we propose a soft margin-based ranking loss for effectively training the KRC model, which characterizes different semantic distances between negative and positive triplets. Furthermore, we combine regularities with distributional representations to predict the missing triplets, which can possess certain robust guarantee. We evaluate our method on the tasks of knowledge graph completion and entity classification. Extensive experiments show that KRC achieves a better, or comparable performance against state-of-the-art methods. Besides, KRC makes a great improvement when dealing with long-tail entities, which have few instances in the knowledge graph. (C) 2020 Elsevier B.V. All rights reserved. |
关键词 | Knowledge graph embedding Translational model Relational constraints Knowledge graph completion |
DOI | 10.1016/j.neucom.2020.12.012 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61876183] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000615368600008 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 知识表示与推理 |
国重实验室规划方向分类 | 可解释人工智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/43175 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
通讯作者 | Zhang, Wensheng |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China |
通讯作者单位 | 精密感知与控制研究中心 |
推荐引用方式 GB/T 7714 | Li, Mingda,Sun, Zhengya,Zhang, Siheng,et al. Enhancing knowledge graph embedding with relational constraints[J]. NEUROCOMPUTING,2021,429:77-88. |
APA | Li, Mingda,Sun, Zhengya,Zhang, Siheng,&Zhang, Wensheng.(2021).Enhancing knowledge graph embedding with relational constraints.NEUROCOMPUTING,429,77-88. |
MLA | Li, Mingda,et al."Enhancing knowledge graph embedding with relational constraints".NEUROCOMPUTING 429(2021):77-88. |
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