OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph
Embedding
- URL: http://arxiv.org/abs/2105.07688v1
- Date: Mon, 17 May 2021 09:18:56 GMT
- Title: OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph
Embedding
- Authors: Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Zhenxi Lin, Yefeng
Zheng
- Abstract summary: We propose an ontological-guided entity alignment method named OntoEA.
Experiments on seven public and industrial benchmarks have demonstrated the state-of-the-art performance of OntoEA.
- Score: 22.47525303095817
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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