知识图谱实体对齐研究综述:从传统方法到前沿技术OA

With the development of Internet and big data technologies,knowledge graphs,as a critical structured tool for describing entities and their relationships,have been widely applied across multiple domains.The entity alignment task in knowledge graphs aims to integrate entity information from heterogeneous knowledge graphs,addressing data silos and significantly enhancing the quality of knowledge graph construction and enabling cross-domain applications.This paper provides a comprehensive review of research progress in knowledge graph entity alignment.Firstly,it introduces the fundamental concepts and types of knowledge graphs.Subsequently,it elaborates on traditional entity alignment methods,including feature similarity-based approaches,machine learning-based techniques,and reasoning-based methodologies.The paper then focuses on entity alignment methods leveraging knowledge representation learning technologies,and explores challenges in aligning entities within multimodal and temporal knowledge graphs.Finally,it discusses the broad prospects of entity alignment in natural language processing and intelligent applications,as well as the potential of com-bining existing methods with emerging technologies to improve alignment accuracy and efficiency.