Title
Jointly Learning Entity and Relation Representations for Entity Alignment
Abstract
Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs). Recent developments in the field often take an embedding-based approach to model the structural information of KGs so that entity alignment can be easily performed in the embedding space. However, most existing works do not explicitly utilize useful relation representations to assist in entity alignment, which, as we will show in the paper, is a simple yet effective way for improving entity alignment. This paper presents a novel joint learning framework for entity alignment. At the core of our approach is a Graph Convolutional Network (GCN) based framework for learning both entity and relation representations. Rather than relying on pre-aligned relation seeds to learn relation representations, we first approximate them using entity embeddings learned by the GCN. We then incorporate the relation approximation into entities to iteratively learn better representations for both. Experiments performed on three real-world cross-lingual datasets show that our approach substantially outperforms state-of-the-art entity alignment methods.
Year
DOI
Venue
2019
10.18653/v1/D19-1023
EMNLP/IJCNLP (1)
DocType
Volume
Citations 
Conference
D19-1
4
PageRank 
References 
Authors
0.46
0
5
Name
Order
Citations
PageRank
Yuting Wu1101.24
Xiao Liu2101.24
Yansong Feng373564.17
Zheng Wang4414.27
Dongyan Zhao599896.35