Semantic-aware entity alignment for low resource language knowledge graph

Abstract

Entity alignment (EA) is an important technique aiming to find the same real entity between two different source knowledge graphs (KGs). Current methods typically learn the embedding of entities for EA from the structure of KGs for EA. Most EA models are designed for rich-resource languages, requiring sufficient resources such as a parallel corpus and pre-trained language models. However, low-resource language KGs have received less attention, and current models demonstrate poor performance on those low-resource KGs. Recently, researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance, but the relation semantics are often ignored. To address these issues, we propose a novel Semantic-aware Graph Neural Network (SGNN) for entity alignment. First, we generate pseudo sentences according to the relation triples and produce representations using pre-trained models. Second, our approach explores semantic information from the connected relations by a graph neural network. Our model captures expanded feature information from KGs. Experimental results using three low-resource languages demonstrate that our proposed SGNN approach out performs better than state-of-the-art alignment methods on three proposed datasets and three public datasets.