摘要
知识图谱为自适应学习系统中的领域知识建模提供了新思路,但现有构建方法缺乏人类智慧与机器智能的有效结合,无法提供高质量、低成本的知识图谱。基于此,文章首先从人机协同视角出发,提出了自适应学习系统中知识图谱的构建方法,具体包括知识图谱模式设计、资源获取与预处理、知识图谱知识元抽取、知识元语义关系挖掘、知识图谱融合等五大环节;随后,文章以"人工智能"学科为例,对构建方法进行了初步验证;最后,文章从碎片化学习资源整合、适应性学习支持服务两个维度,进一步阐述了知识图谱在自适应学习系统中的应用。文章的研究对于开展基于自适应学习系统的大规模个性化学习具有重要意义。
Knowledge graph provides new thought for domain knowledge modeling in adaptive learning system, while the existing construction methods lack the effective combination of human intelligence and machine intelligence, and cannot provide high-quality and low-cost knowledge graph. Based on this, this paper put forward the construction method of knowledge graph in adaptive learning system from the perspective of human-machine collaboration, including the five links of pattern design of knowledge graph, resource acquisition and pretreatment, the extraction of knowledge element in knowledge graph, the excavation of semantic relationship of element and knowledge graph integration. Then, this paper took the “Artificial Intelligence” as a case to preliminarily verify the construction method. Finally, this paper further elaborated the application of knowledge graph in adaptive learning system from two dimensions of the integration of fragmented learning resources and the support services of adaptive learning. The research of this paper was of great significance for implementing large-scale personalized learning based on adaptive learning system.
作者
李振
董晓晓
周东岱
童婷婷
LI Zhen;DONG Xiao-xiao;ZHOU Dong-dai;TONG Ting-ting(School of Information Science and Technology,Northeast Normal University,Changchun,Jilin,China 130117)
出处
《现代教育技术》
CSSCI
北大核心
2019年第10期80-86,共7页
Modern Educational Technology
基金
国家自然科学基金面上资助项目“基于深度学习的自适应学习系统关键技术研究”(项目编号：61977015)的阶段性研究成果
关键词
知识图谱
人机协同
知识元抽取
语义关系挖掘
knowledge graph
human-machine cooperation
knowledge element extraction
semantic relation mining
