Paper Info
Title
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding.
Abstract
Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embedding-based models, and the conventional reasoning and lexical matching based systems. The former compute the similarity of entities via their cross-KG embeddings, but they usually rely on an ideal supervised learning setting for good performance and lack appropriate reasoning to avoid logically wrong mappings; while the latter address the reasoning issue but are poor at utilizing the KG graph structures and the entity contexts. In this study, we aim at combining the above two solutions and thus propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. It learns the KG embeddings via entity mappings from a probabilistic reasoning system named PARIS, and feeds the resultant entity mappings and embeddings back into PARIS for augmentation. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.
Year
DOI
Venue
2021
10.24963/ijcai.2021/278
IJCAI
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Zhiyuan Qi1524.71
Ziheng Zhang202.03
J Chen313930.64
Xi Chen4358.36
Yuejia Xiang502.37
Ningyu Zhang66318.56
Yefeng Zheng71391114.67