Modeling the Correlations of Relations for Knowledge Graph Embedding
文献类型：期刊论文
| 作者 | Zhu, Ji-Zhao1,2; Jia, Yan-Tao1; Xu, Jun1; Qiao, Jian-Zhong2; Cheng, Xue-Qi1 |
| 刊名 | JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2018-03-01 |
| 卷号 | 33期号:2页码:323-334 |
| 关键词 | knowledge graph embedding low-rank matrix decomposition |
| ISSN号 | 1000-9000 |
| DOI | 10.1007/s11390-018-1821-8 |
| 英文摘要 | Knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods include TransE, TransH, and TransR. All these methods map different relations into the vector space separately and the intrinsic correlations of these relations are ignored. It is obvious that there exist some correlations among relations because different relations may connect to a common entity. For example, the triples (Steve Jobs, PlaceOfBrith, California) and (Apple Inc., Location, California) share the same entity California as their tail entity. We analyze the embedded relation matrices learned by TransE/TransH/TransR, and find that the correlations of relations do exist and they are showed as low-rank structure over the embedded relation matrix. It is natural to ask whether we can leverage these correlations to learn better embeddings for the entities and relations in a knowledge graph. In this paper, we propose to learn the embedded relation matrix by decomposing it as a product of two low-dimensional matrices, for characterizing the low-rank structure. The proposed method, called TransCoRe (Translation-Based Method via Modeling the Correlations of Relations), learns the embeddings of entities and relations with translation-based framework. Experimental results based on the benchmark datasets of WordNet and Freebase demonstrate that our method outperforms the typical baselines on link prediction and triple classification tasks. |
| 资助项目 | National Basic Research 973 Program of China[2014CB340405] ; National Key Research and Development Program of China[2016YFB1000902] ; National Natural Science Foundation of China[61402442] ; National Natural Science Foundation of China[61272177] ; National Natural Science Foundation of China[61173008] ; National Natural Science Foundation of China[61232010] ; National Natural Science Foundation of China[61303244] ; National Natural Science Foundation of China[61572469] ; National Natural Science Foundation of China[91646120] ; National Natural Science Foundation of China[61572473] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000428379000007 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/5975]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Xu, Jun; Qiao, Jian-Zhong |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Key Lab Network Data Sci & Technol, Beijing 110190, Peoples R China 2.Northeastern Univ, Coll Comp Sci & Engn, Shenyang 110169, Liaoning, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Ji-Zhao,Jia, Yan-Tao,Xu, Jun,et al. Modeling the Correlations of Relations for Knowledge Graph Embedding[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2018,33(2):323-334. |
| APA | Zhu, Ji-Zhao,Jia, Yan-Tao,Xu, Jun,Qiao, Jian-Zhong,&Cheng, Xue-Qi.(2018).Modeling the Correlations of Relations for Knowledge Graph Embedding.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,33(2),323-334. |
| MLA | Zhu, Ji-Zhao,et al."Modeling the Correlations of Relations for Knowledge Graph Embedding".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 33.2(2018):323-334. |
入库方式： OAI收割
来源：计算技术研究所
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