计算机工程与应用2023，Vol.59Issue(24)：70-77,8.DOI:10.3778/j.issn.1002-8331.2302-0087
联合三元组嵌入的实体对齐
Joint Triple Embedding for Entity Alignment
摘要
Abstract
Entity alignment is the task of identifying entities from different knowledge graphs that point to the same item and is important for KG fusion.Most of the existing approaches are based on graph neural networks that learn the entities embedding by modeling the neighborhood information of the entities.However,graph neural networks-based approaches have difficulty learning triples information in knowledge graphs,the triples information is not sufficiently utilized.In order to solve this problem,an entity alignment model with joint triple embedding is proposed in this paper.The proposed model computes a triple embedding for each entity and then uses this triple embedding for entity alignment.In addition,consider-ing that the relations in the knowledge graphs have different types,in order to exploit these relation types,a relation type-aware calculation method of triple embedding is proposed.Meanwhile,constraints based on relation types are added to this model,to learn the mapping properties of relations.Experiments conducted on three real-world datasets show that this approach outperforms state-of-the-art methods,the effectiveness of the proposed method is verified.关键词
实体对齐/图卷积网络/三元组/知识图谱Key words
entity alignment/graph convolutional networks/triple/knowledge graph分类
信息技术与安全科学引用本文复制引用
李凤英,黎家鹏..联合三元组嵌入的实体对齐[J].计算机工程与应用,2023,59(24):70-77,8.基金项目
国家自然科学基金(62062029). （62062029）
