国家自然科学基金面上项目(62077014); 湖南省教育厅科学研究项目优秀青年项目(18B037); 湖南省自然科学基金(2020JJ4440); 教育部人文社会科学研究青年基金(18YJCZH124)
面对当前日益庞大的教育大数据, 如何在海量数据中高效、准确地提取出高价值的知识, 以满足个性化教学需求, 已成为当前智慧教育的一个研究热点. 作为一种可视化分析技术, 知识图谱可有效构建和挖掘知识及知识间的相互联系, 现已成功应用于诸多领域. 而图嵌入技术的引入, 则有利于提升大数据背景下知识图谱的处理效率. 针对个性化教育的知识处理需求, 首先介绍了知识图谱与图嵌入算法的基本概念, 并从向量平移、基于张量因式分解和基于神经网络等3个方面, 介绍基于三元组的表征学习模型. 然后, 从7种应用类型的角度, 综述知识图谱与图嵌入技术在个性化教育领域中的研究现状. 最后, 总结全文并给出未来的研究展望.
At present, big data regarding education are increasingly growing. How to efficiently and accurately extract high-value knowledge from the massive data to meet the personalized education needs of learners or educators is a hot topic worthy of attention in smart education. As a visual analysis technology, knowledge graphs can effectively construct and mine knowledge and the interrelationship between knowledge, which has been successfully applied in many fields. The introduction of graph embedding technology is beneficial to significantly improve the processing efficiency of knowledge graphs in the context of big data. To meet the knowledge processing needs of personalized education, this paper first introduces the basic concepts of knowledge graph and graph embedding algorithms and then expounds the triple-based representation learning model from three aspects: vector translation, tensor-based factorization, and neural network-based representation learning. Then, from the perspective of seven application types, the practical application of knowledge graph and graph embedding in the field of personalized education is reviewed. Finally, the paper is summarized and the directions of future research are discussed.