课程知识图谱构建技术研究综述

doi:10.19678/j.issn.1000-3428.0069543

摘要/Abstract

摘要：

在教育信息化持续推进的背景下, 构建精准且高效的课程知识图谱已成为推动教育个性化发展的关键任务之一。课程知识图谱作为一种结构化的知识表示模型, 旨在揭示课程内容与学习目标之间的复杂关联关系, 以优化教育资源配置, 并为学习者定制个性化的学习路径。围绕课程知识图谱的构建技术进行探讨, 首先阐述知识图谱、教育知识图谱、课程知识图谱的基本概念及其之间的内在联系与显著差异; 其次深入剖析课程知识图谱构建的关键技术, 涵盖课程本体设计、实体抽取、关系抽取等方面, 并对其发展历程、特点及局限性展开详细分析与总结; 再次, 探讨课程知识图谱在学习资源推荐、学习者画像建模和多模态课程知识图谱构建等场景中的应用价值; 最后, 聚焦于课程知识图谱在构建过程中所面临的难题, 如数据多样性和异构性、知识图谱质量难以评估以及多课程交叉融合不足等, 从深度学习、大语言模型(LLM)等前沿技术的角度出发, 对未来的发展趋势进行展望。

关键词: 课程知识图谱, 知识图谱构建, 实体抽取, 关系抽取, 深度学习

Abstract:

In the context of ongoing advancements in educational informatization, constructing precise and efficient curriculum knowledge graphs has become key to promoting personalized education development. As a structured knowledge representation model, curriculum knowledge graphs reveal complex relations between curriculum content and learning objectives to optimize the allocation of educational resources, and tailoring personalized learning paths for learners. This survey presents a discussion around the techniques used to construct curriculum knowledge graphs, starting with an explanation of the basic concepts; intrinsic connections; and significant differences among general, educational, and curriculum knowledge graphs. It then delves into the key technologies used for building curriculum knowledge graphs, covering aspects such as curriculum ontology design, entity extraction, and relation extraction, and provides a detailed analysis and summary of their evolution, key features, and limitations. Furthermore, it explores the application value of curriculum knowledge graphs in scenarios such as learning resource recommendation, learner behavior profile and modeling, and multimodal curriculum knowledge graph construction. Finally, it focuses on the challenges in constructing curriculum knowledge graphs, such as data diversity and heterogeneity, difficulties in quality evaluation, and the lack of cross-curriculum integration, and provides future-oriented insights based on cutting-edge technologies such as deep learning and Large Language Models (LLMs).

Key words: curriculum knowledge graph, knowledge graph construction, entity extraction, relation extraction, deep learning

孙丽郡, 孟繁军, 徐行健. 课程知识图谱构建技术研究综述[J]. 计算机工程, 2025, 51(11): 1-21.

SUN Lijun, MENG Fanjun, XU Xingjian. Survey of Research on Curriculum Knowledge Graph Construction Techniques[J]. Computer Engineering, 2025, 51(11): 1-21.

https://www.ecice06.com/CN/Y2025/V51/I11/1

图/表 13

图1 课程知识图谱示例

Fig.1 Example of curriculum knowledge graph

图2 课程知识图谱构建过程

Fig.2 Curriculum knowledge graph construction process

图3 多模态数据对齐方法

Fig.3 Multi-modal data alignment method

图4 BERT-BiLSTM-CRF模型结构

Fig.4 Structure of BERT-BiLSTM-CRF model

图5 深度学习实体抽取模型的发展历程

Fig.5 Development history of deep learning entity extraction models

图6 基于课程知识图谱的应用方向

Fig.6 Application directions based on curriculum knowledge graph

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