计算机工程2026，Vol.52Issue(8)：84-100,17.DOI:10.19678/j.issn.1000-3428.0252971
知识图谱构建技术研究综述
Research Review of Knowledge Graph Construction Technology
摘要
Abstract
Knowledge Graphs(KGs)are structured semantic knowledge representations with entities as nodes and relations as edges.They accurately depict various things in the real world and their complex associations and have become a core supporting technology across multiple domains,including artificial intelligence,natural language processing,recommendation systems,and intelligent question-answering.Hence,they form an important foundation for machines to understand semantics and achieve cognitive intelligence.This paper describes the basic concepts and system architecture of a KG,clarifies the knowledge representation unit with the"entity-relation-attribute"triple as the core,and analyzes the applicable scenarios and technical characteristics of both top-down and bottom-up construction approaches.The technical evolution of three core links in the KG construction process,namely,information extraction,knowledge fusion,and knowledge reasoning,are analyzed.Subsequently,the technical development context is systematically discussed,and the advantages and limitations of different methods are discussed.Further,through an in-depth analysis of the differences in technical route selection between DBpedia and Baidu as two typical KGs,the theoretical method is combined with an actual KG construction scenario.Finally,the challenges faced by current KG construction in terms of data quality,semantic consistency,and dynamic evolution are summarized,and future research directions are examined.This review provides comprehensive guidance for both theoretical research and practical applications in KG construction,thereby advancing technological development in this field.关键词
知识图谱/信息抽取/知识融合/知识推理/深度学习Key words
Knowledge Graph(KG)/information extraction/knowledge fusion/knowledge reasoning/deep learning分类
信息技术与安全科学引用本文复制引用
赵莹莹,朱率率..知识图谱构建技术研究综述[J].计算机工程,2026,52(8):84-100,17.基金项目
国防科技自主科研项目重点课题(ZZKY20243102). （ZZKY20243102）
