An effective knowledge graph entity alignment model based on multiple information. (May 2023)

• Record Type: Journal Article
• Authors: Zhu, Beibei; Bao, Tie; Han, Ridong; Cui, Hai; Han, Jiayu; Liu, Lu; Peng, Tao
• Is Part Of: Neural networks. Volume 162(2023)
• Page Start: 83
• Page End: 98
• Publication Date: 2023-05
• Subjects: Entity alignment -- Knowledge graph -- Structure -- Semantic -- String; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2023.02.029
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Entity alignment refers to matching entities with the same realistic meaning in different knowledge graphs. The structure of a knowledge graph provides the global signal for entity alignment. But in the real world, a knowledge graph provides insufficient structural information in general. Moreover, the problem of knowledge graph heterogeneity is common. The semantic and string information can alleviate the problems caused by the sparse and heterogeneous nature of knowledge graphs, yet both of them have not been fully utilized by most existing work. Therefore, we propose an e ntity a lignment model based on m ultiple i nformation (EAMI), which employs structural, semantic and string information. EAMI learns the structural representation of a knowledge graph by using multi-layer graph convolutional networks. To acquire more accurate entity vector representation, we incorporate the attribute semantic representation into the structural representation. In addition, to further improve entity alignment, we study the entity name string information. There is no training required to calculate the similarity of entity names. Our model is tested on publicly available cross-lingual datasets and cross-resource datasets, and the experimental results demonstrate the effectiveness of our model.