Title
OntoEA - Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding.
Abstract
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities. Current methods have explored and utilized the graph structure, the entity names and attributes, but ignore the ontology (or ontological schema) which contains critical meta information such as classes and their membership relationships with entities. In this paper, we propose an ontology-guided entity alignment method named OntoEA, where both KGs and their ontologies are jointly embedded, and the class hierarchy and the class disjointness are utilized to avoid false mappings. Extensive experiments on seven public and industrial benchmarks have demonstrated the state-of-the-art performance of OntoEA and the effectiveness of the ontologies.
Year
Venue
DocType
2021
ACL/IJCNLP
Conference
Volume
Citations 
PageRank 
2021.findings-acl
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yuejia Xiang102.37
Ziheng Zhang202.03
J Chen313930.64
Xi Chen4358.36
Zhenxi Lin511.36
Yefeng Zheng61391114.67