Knowledge Commons of Institute of Automation,CAS
Improve the translational distance models for knowledge graph embedding | |
Zhang, Siheng1,2; Sun, Zhengya1; Zhang, Wensheng1,3 | |
发表期刊 | JOURNAL OF INTELLIGENT INFORMATION SYSTEMS |
ISSN | 0925-9902 |
2020-01-27 | |
卷号 | 2020期号:1页码:23 |
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. |
关键词 | Knowledge graph embedding Translational distance model Positional encoding Self-attention |
DOI | 10.1007/s10844-019-00592-7 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2016QY03D0500] ; National Natural Science Foundation of China[61976212] ; National Natural Science Foundation of China[61876183] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61876183] ; National Natural Science Foundation of China[61976212] ; National Key Research and Development Program of China[2016QY03D0500] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems |
WOS记录号 | WOS:000515591800001 |
出版者 | SPRINGER |
七大方向——子方向分类 | 知识表示与推理 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/38399 |
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, Res Ctr Precis Sensing & Control, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Foshan Univ, Sch Math & Big Data, Foshan, 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,2020,2020(1):23. |
APA | Zhang, Siheng,Sun, Zhengya,&Zhang, Wensheng.(2020).Improve the translational distance models for knowledge graph embedding.JOURNAL OF INTELLIGENT INFORMATION SYSTEMS,2020(1),23. |
MLA | Zhang, Siheng,et al."Improve the translational distance models for knowledge graph embedding".JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2020.1(2020):23. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Zhang_TransAP_JIIS.p(802KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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