Cross-lingual knowledge graph entity alignment by aggregating extensive structures and specific semantics

Abstract

Entity alignment aims to link entities from different knowledge graphs (KGs) that refer to the same real-world identity. Recently, embedding-based approaches that primarily center on topological structures get close attention in this field. Even achieving promising performance, these approaches overlook the vital impact of entity-specific semantics on entity alignment tasks. In this paper, we propose a new framework SSEA (Extensive Structures and Specific Semantics for Entity Alignment), which jointly employs extensive structures and specific semantics to boost the performance of entity alignment. Specifically, we employ graph convolution networks (GCNs) to learn the representations of entity structures. Besides considering entity representations, we also explore relation semantics by approximating relation embeddings based on head entity and tail entity representations. Moreover, attribute semantics are also learned by GCNs while they are independent of joint entity and relation embeddings. The results of structure, relation, and attribute representations are concatenated for better entity alignment. Experimental results on three benchmark datasets from real-world KGs demonstrate that our approach has achieved promising performance in most cases. Notably, SSEA has achieved 91.78 and 97.20 for metrics Hits@1 and Hits@10 respectively on the $DBP15K_{FR-EN}$ dataset.