.png){kind=icon}
返回
Knowledge graph embedding with shared latent semantic units
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2021.02.013.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Knowledge graph embedding (KGE) aims to project both entities and relations into a continuous low-dimensional space. However, for a given knowledge graph (KG), only a small number of entities and relations occur many times, while the vast majority of entities and relations occur less frequently. This data sparsity problem has largely been ignored by most of the existing KGE models. To this end, in this paper, we propose a general technique to enable knowledge transfer among semantically similar entities or relations. Specifically, we define latent semantic units (LSUs), which are the sub-components of entity and relation embeddings. Semantically similar entities or relations are supposed to share the same LSUs, and thus knowledge can be transferred among entities or relations. Finally, extensive experiments show that the proposed technique is able to enhance existing KGE models and can provide better representations of KGs. (C) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Knowledge graph
Reinforcement learning
Embedding
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
7.8K
被引数:
3.0W
机构
引用论文
Kinematic analysis of limb movements in neuropsychological research: Subtle deficits and recovery of function.神经心理学研究中肢体运动的运动学分析: 细微的缺陷和功能的恢复。
.png){kind=icon}
PREAI
分享收藏
分享收藏
分享收藏
查看更多内容