.png){kind=icon}
.png){kind=icon}
Return
Kernel multi-attention neural network for knowledge graph embedding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2021.107188.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Link prediction is the problem of predicting missing link between entities and relations for knowledge graph. In recent years, some tasks have achieved great success for link prediction, but these tasks are far from expanding entity relation vectors, and cannot predict missing links more efficiently. In this paper, we propose a novel link prediction method called kernel multi-attention neural network for knowledge graph embedding (KMAE) which is able to extend kernel separately in entity and relation attributes. The kernel function uses Gaussian kernel function to expand into more robust entity kernel and relation kernel. In addition, we constructed a novel multi-attention neural network that acts on the entity kernel and relation kernel which can capture local important characteristics. Experiments on FB15k-237 and WN18RR, show that multi-attention fully reflect excellent performance in the task of knowledge graph embedding. Our proposed KMAE achieves better results than previous state-of-the-art link prediction methods. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Link prediction
Entity kernel
Relation kernel
Multi-attention neural network
Knowledge graph embedding
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W