Global Structure and Local Semantics-Preserved Embeddings for Entity Alignment
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 3658-3664. https://doi.org/10.24963/ijcai.2020/506
Entity alignment (EA) aims to identify entities located in different knowledge graphs (KGs) that refer to the same real-world object. To learn the entity representations, most EA approaches rely on either translation-based methods which capture the local relation semantics of entities or graph convolutional networks (GCNs), which exploit the global KG structure. Afterward, the aligned entities are identified based on their distances. In this paper, we propose to jointly leverage the global KG structure and entity-specific relational triples for better entity alignment. Specifically, a global structure and local semantics preserving network is proposed to learn entity representations in a coarse-to-fine manner. Experiments on several real-world datasets show that our method significantly outperforms other entity alignment approaches and achieves the new state-of-the-art performance.
Natural Language Processing: Embeddings
Natural Language Processing: Named Entities
Natural Language Processing: Natural Language Processing