RAGAT: Relation Aware Graph Attention Network for Knowledge Graph Completion
Knowledge graph completion (KGC) is the task of predicting missing links based on known triples for knowledge graphs. Several recent works suggest that Graph Neural Networks (GNN) that exploit graph structures achieve promising performance on KGC. These models learn information called messages from...
Main Authors: | Xiyang Liu, Huobin Tan, Qinghong Chen, Guangyan Lin |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9340326/ |
Similar Items
-
Graph Attention Networks With Local Structure Awareness for Knowledge Graph Completion
by: Kexi Ji, et al.
Published: (2020-01-01) -
An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding
by: Su Jeong Choi, et al.
Published: (2020-04-01) -
Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion
by: Peiyuan Qiu, et al.
Published: (2019-05-01) -
Knowledge Graph Embedding via Graph Attenuated Attention Networks
by: Rui Wang, et al.
Published: (2020-01-01) -
A Survey on Knowledge Graph Embeddings for Link Prediction
by: Meihong Wang, et al.
Published: (2021-03-01)