Cross-lingual entity alignment based on complex relationships and fine-grained attributes

Knowledge graph entity alignment seeks to match equivalent entities across different graphs, a critical task for enabling cross-lingual knowledge fusion. Mainstream methods use representation learning for entity alignment based on vector distances, but struggle with complex relational semantics and underutilize fine-grained attribute information crucial for alignment. To overcome the above problems, this paper proposes a cross-lingual entity alignment model based on complex relationships and fine-grained attributes (CEARA). The proposed model effectively handles relational semantics by distinguishing their varying impacts on entity embeddings and extracting detailed attribute information to enhance alignment accuracy. Additionally, it integrates entity name string similarity to complement missing or noisy relational and attribute data, further improving alignment reliability. To mitigate alignment conflicts, the model employs a global alignment strategy. Experimental results on three cross-lingual datasets demonstrate that CEARA not only outperforms representative baseline models but also achieves Hits@1 scores exceeding 95% across all datasets, highlighting its effectiveness and robustness for cross-lingual alignment. This paper contributes to the advancement of cross-lingual knowledge discovery and application.