Entity-Alignment Interaction Model Based on Chinese RoBERTa

Abstract

Entity alignment aims to match entities with the same semantics from different knowledge graphs. Most existing studies use neural networks to combine graph-structure information and additional entity information (such as names, descriptions, images, and attributes) to achieve entity alignment. However, due to the heterogeneity of knowledge graphs, aligned entities often do not have the same neighbors, which makes it difficult to utilize the structural information from knowledge graphs and results in a decrease in alignment accuracy. Therefore, in this paper, we propose an interaction model that exploits only the additional information on entities. Our model utilizes names, attributes, and neighbors of entities for interaction and introduces attention interaction to extract features to further evaluate the matching scores between entities. Our model is applicable to Chinese datasets, and experimental results show that it has achieved good results on the Chinese medical datasets denoted MED-BBK-9K.