知识图谱构建技术研究综述

doi:10.19678/j.issn.1000-3428.0252971

摘要/Abstract

摘要：

知识图谱(KG)作为一种以实体为节点、关系为边的结构化语义知识表示形式, 能够精准刻画现实世界中各类事物及其复杂关联, 已成为人工智能、自然语言处理、推荐系统、智能问答等多个领域的核心支撑技术, 为机器理解语义和实现认知智能提供了重要基础。首先, 阐述知识图谱的基本概念与体系架构, 明确以"实体-关系-属性"三元组为核心的知识表示单元, 并分别剖析自顶向下和自底向上两种构建模式的适用场景与技术特点; 其次, 重点分析知识图谱构建过程中信息抽取、知识融合以及知识推理三大核心环节的技术演进, 系统梳理技术发展脉络, 并对比不同方法的优势与局限; 再次, 深入剖析DBpedia和百度两个典型知识图谱在技术路线选择上的差异, 将理论方法与实际知识图谱构建场景相结合; 最后, 总结当前知识图谱构建在数据质量、语义一致性、动态演化等方面面临的挑战, 并展望未来研究方向, 旨在为知识图谱构建的理论研究与实际应用提供全面参考, 推动该领域技术的进一步发展。

关键词: 知识图谱, 信息抽取, 知识融合, 知识推理, 深度学习

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.

Key words: Knowledge Graph (KG), information extraction, knowledge fusion, knowledge reasoning, deep learning

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.

ZHAO Yingying, ZHU Shuaishuai. Research Review of Knowledge Graph Construction Technology[J]. Computer Engineering, 2026, 52(8): 84-100.

https://www.ecice06.com/CN/Y2026/V52/I8/84

图/表 7

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