计算机工程2025，Vol.51Issue(2)：18-34,17.DOI:10.19678/j.issn.1000-3428.0068386
知识图谱嵌入研究进展综述
Review of Research Progress on Knowledge Graph Embedding
摘要
Abstract
With the continuous development of big data and artificial intelligence technologies,knowledge graph embedding is developing rapidly,and knowledge graph applications are becoming increasingly widespread.Knowledge graph embedding improves the efficiency of knowledge representation and reasoning by representing structured knowledge into a low-dimensional vector space.This study provides a comprehensive overview of knowledge graph embedding technology,including its basic concepts,model categories,evaluation indices,and application prospects.First,the basic concepts and background of knowledge graph embedding are introduced,classifying the technology into four main categories:embedding models based on translation mechanisms,semantic-matching mechanisms,neural networks,and additional information.The core ideas,scoring functions,advantages and disadvantages,and application scenarios of the related models are meticulously sorted.Second,common datasets and evaluation indices of knowledge graph embedding are summarized,along with application prospects,such as link prediction and triple classification.The experimental results are analyzed,and downstream tasks,such as question-and-answer systems and recommenders,are introduced.Finally,the knowledge graph embedding technology is reviewed and summarized,outlining its limitations and the primary existing problems while discussing the opportunities and challenges for future knowledge graph embedding along with potential research directions.关键词
知识图谱/知识图谱嵌入/知识图谱表示学习/链接预测/三元组分类Key words
knowledge graph/knowledge graph embedding/knowledge graph representation learning/link prediction/triple classification分类
计算机与自动化引用本文复制引用
马恒志,钱育蓉,冷洪勇,吴海鹏,陶文彬,张依杨..知识图谱嵌入研究进展综述[J].计算机工程,2025,51(2):18-34,17.基金项目
国家自然科学基金(61966035,62266043). （61966035,62266043）
