Improve the translational distance models for knowledge graph embedding
文献类型：期刊论文
| 作者 | Zhang, Siheng2,3 ; Sun, Zhengya2; Zhang, Wensheng1,2
|
| 刊名 | JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
 |
| 出版日期 | 2020-01-27 |
| 页码 | 23 |
| 关键词 | Knowledge graph embedding Translational distance model Positional encoding Self-attention |
| ISSN号 | 0925-9902 |
| DOI | 10.1007/s10844-019-00592-7 |
| 通讯作者 | Zhang, Wensheng(wensheng.zhang@ia.ac.cn) |
| 英文摘要 | Knowledge graph embedding techniques can be roughly divided into two mainstream, translational distance models and semantic matching models. Though intuitive, translational distance models fail to deal with the circle structure and hierarchical structure in knowledge graphs. In this paper, we propose a general learning framework named TransX-pa, which takes various models (TransE, TransR, TransH and TransD) into consideration. From this unified viewpoint, we analyse the learning bottlenecks are: (i) the common assumption that the inverse of a relation r is modelled as its opposite - r; and (ii) the failure to capture the rich interactions between entities and relations. Correspondingly, we introduce position-aware embeddings and self-attention blocks, and show that they can be adapted to various translational distance models. Experiments are conducted on different datasets extracted from real-world knowledge graphs Freebase and WordNet in the tasks of both triplet classification and link prediction. The results show that our approach makes a great improvement, showing a better, or comparable, performance with state-of-the-art methods. |
| 资助项目 | National Key Research and Development Program of China[2016QY03D0500] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61876183] ; National Natural Science Foundation of China[61976212] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000515591800001 |
| 出版者 | SPRINGER |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/38398]  |
| 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.Foshan Univ, Sch Math & Big Data, Foshan, Peoples R China 2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Siheng,Sun, Zhengya,Zhang, Wensheng. Improve the translational distance models for knowledge graph embedding[J]. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS,2020:23. |
| APA | Zhang, Siheng,Sun, Zhengya,&Zhang, Wensheng.(2020).Improve the translational distance models for knowledge graph embedding.JOURNAL OF INTELLIGENT INFORMATION SYSTEMS,23. |
| MLA | Zhang, Siheng,et al."Improve the translational distance models for knowledge graph embedding".JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2020):23. |
入库方式： OAI收割
来源：自动化研究所
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