.png){kind=icon}
.png){kind=icon}
Return
AEP: Aligning knowledge graphs via embedding propagation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2022.08.018.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Knowledge graph alignment aims to identify entity pairs having the same meaning between different knowledge graphs, which is essential to the automated construction of a coherent knowledge base. With the development of knowledge representation learning, researchers have proposed several useful embedding-based alignment methods. In practice, some entities are easily aligned with high confidence while others are not. However, there is a lack of effective methods which boost the alignment of implicitly aligned entities based on explicitly aligned ones. This paper presents AEP, which combines several effective schemes for accurate knowledge graph alignment. First, we employ a word-embedding model to encode the semantic information contained in entities' surface names. We propose an attentionbased graph convolutional network model to incorporate the structural information via supervised embedding propagation. Besides, we develop a multi-view bootstrapping strategy to address the overfitting problem caused by insufficient training sets. Next, an embedding-propagation-based alignment scheme is proposed to improve the alignment accuracy by propagating explicitly aligned entities' embeddings to implicitly aligned ones in an unsupervised manner. Finally, we conduct extensive experiments to validate the superiority of AEP and evaluate the effects of proposed schemes. Experimental results show that AEP outperforms state-of-the-art methods, and the proposed schemes improve the alignment accuracy significantly. (c) 2022 Published by Elsevier B.V.
Keywords:
Knowledge graphs
Entity alignment
Embedding propagation