Review on Application of Knowledge Mapping and Graph Embedding in Personalized Education

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments

    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.

    Cited by
Get Citation


Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
  • Received:May 04,2021
  • Revised:May 28,2021
  • Adopted:
  • Online: January 24,2022
  • Published:
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a)
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063