Paper Info
Title
Relational Graph Neural Network With Hierarchical Attention For Knowledge Graph Completion
Abstract
The rapid proliferation of knowledge graphs (KGs) has changed the paradigm for various AI-related applications. Despite their large sizes, modern KGs are far from complete and comprehensive. This has motivated the research in knowledge graph completion (KGC), which aims to infer missing values in incomplete knowledge triples. However, most existing KGC models treat the triples in KGs independently without leveraging the inherent and valuable information from the local neighborhood surrounding an entity. To this end, we propose a Relational Graph neural network with Hierarchical ATtention (RGHAT) for the KGC task. The proposed model is equipped with a two-level attention mechanism: (i) the first level is the relation-level attention, which is inspired by the intuition that different relations have different weights for indicating an entity; (ii) the second level is the entity-level attention, which enables our model to highlight the importance of different neighboring entities under the same relation. The hierarchical attention mechanism makes our model more effective to utilize the neighborhood information of an entity. Finally, we extensively validate the superiority of RGHAT against various state-of-the-art baselines.
Year
Venue
DocType
2020
national conference on artificial intelligence
Conference
Volume
ISSN
Citations 
34
2159-5399
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Zhao Zhang1706102.46
Fuzhen Zhuang282775.28
Hengshu Zhu382154.61
Zhiping Shi416843.86
Hui Xiong54958290.62
Qing He675480.58