Enhancing knowledge graph embedding with relational constraints
文献类型：期刊论文
| 作者 | Li, Mingda ; Sun, Zhengya; Zhang, Siheng; Zhang, Wensheng1,2
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2021-03-14 |
| 卷号 | 429页码:77-88 |
| 关键词 | Knowledge graph embedding Translational model Relational constraints Knowledge graph completion |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2020.12.012 |
| 通讯作者 | Zhang, Wensheng(zhangwenshengia@hotmail.com) |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61876183] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000615368600008 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/43174]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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