Semantic-aware entity alignment for low resource language knowledge graph

作     者：Junfei TANG Ran SONG Yuxin HUANG Shengxiang GAO Zhengtao YU 

作者机构：Faculty of Information Engineering and AutomationKunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial IntelligenceKunming University of Science and TechnologyKunming 650500China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2024年第18卷第4期

页      面：97-106页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China(Nos.U21B2027,61972186,61732005) Major Science and Technology Projects of Yunnan Province(Nos.202202AD080003,202203AA080004) 

主　　题：graph neural network knowledge graph entity alignment low-resource language 

摘      要：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 models are designed for rich-resource languages,requiring sufficient resources such as a parallel corpus and pre-trained language ***,low-resource language KGs have received less attention,and current models demonstrate poor performance on those low-resource ***,researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance,but the relation semantics are often *** address these issues,we propose a novel Semantic-aware Graph Neural Network(SGNN)for entity ***,we generate pseudo sentences according to the relation triples and produce representations using pre-trained ***,our approach explores semantic information from the connected relations by a graph neural *** model captures expanded feature information from *** 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.