【作 者】：
李艳燕， 张香玲， 李 新， 杜 静
【关 键 词 】：
智慧教育； 学科知识图谱； 人工智能； 构建应用
【栏 目】：
学习环境与资源
【中文摘要】：
人工智能、大数据和物联网是发展智慧教育的基础，学科知识图谱作为一种语义网络，既能增强人工智能的可解释性，又能助力智慧教育体系框架的构建。文章在分析学科知识图谱的内涵、应用案例的基础上，从学科知识图谱助力智慧教育体系框架的构建以及智慧教育生态系统的重构两个方面探讨了学科知识图谱与智慧教育的适切性；从总体流程、学科知识自动获取以及学科知识融合三个方面讨论了学科知识图谱在智慧教育中的构建路径；最后，提出了学科知识图谱在智慧教育中的六大应用场景：学科知识点查询、知识关联查询、学科知识自动问答、学科知识资源推荐、个性化学习路径推荐和查询以及学习兴趣迁移，并分析了学科知识图谱在智慧教育应用中面临的三大挑战：学科知识验证挑战、学科知识融合挑战以及学科知识图谱的自适应可视化挑战，期望为学科知识图谱在智慧教育中的应用提供借鉴与启示。
【英文摘要】：
Artificial intelligence, big data and the Internet of things are the basis for the development of smart education. As a semantic network, discipline knowledge graphs can not only enhance the interpretability of artificial intelligence, but also facilitate the construction of the architecture of smart education system. Based on the analysis of the connotation and application cases of discipline knowledge graphs, this paper discusses the propriety of discipline knowledge graphs and smart education from the aspects: the construction of smart education system architecture and the reconstruction of smart education ecosystem. Then from three aspects of the overall process, automatic acquisition of discipline knowledge and fusion of discipline knowledge, this paper discusses the construction of discipline knowledge graphs in smart education. Finally this paper puts forward six applications of discipline knowledge graphs in smart education: discipline knowledge query, knowledge associated query, discipline knowledge automatic question-answering, resource recommendation, personalized learning path recommendation and query, and learning interest transfer. It also analyzes three major challenges in the applications of discipline knowledge graphs in smart education, including discipline knowledge verification, discipline knowledge fusion and the adaptive visualization of discipline knowledge graphs. It is expected to provide reference and inspiration for the applications of discipline knowledge graphs in smart education.