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Volume 36 Issue 5
September  2021
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Fu-Rong Dang, Jin-Tao Tang, Kun-Yuan Pang, Ting Wang, Sha-Sha Li, Xiao Li. Constructing an Educational Knowledge Graph with Concepts Linked to Wikipedia[J]. Journal of Computer Science and Technology, 2021, 36(5): 1200-1211. DOI: 10.1007/s11390-020-0328-2
Citation: Fu-Rong Dang, Jin-Tao Tang, Kun-Yuan Pang, Ting Wang, Sha-Sha Li, Xiao Li. Constructing an Educational Knowledge Graph with Concepts Linked to Wikipedia[J]. Journal of Computer Science and Technology, 2021, 36(5): 1200-1211. DOI: 10.1007/s11390-020-0328-2
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Constructing an Educational Knowledge Graph with Concepts Linked to Wikipedia

  • 1 College of Computer Science and Technology, National University of Defense Technology, Hunan 410073, China;
    2 Information Center, National University of Defense Technology, Hunan 410073, China

Funds: This work was supported by the National Key Research and Development Program of China under Grant No. 2018YFB1004502, and the National Natural Science Foundation of China under Grant Nos. 61532001, 61702532 and 61303190.
More Information
  • Author Bio:

    Fu-Rong Dang received her M.S. degree in software engineering from National University of Defense Technology, Changsha, in 2019. Her research interests include natural language processing and knowledge graph.

  • Corresponding author:

    Ting Wang E-mail: tingwang@nudt.edu.cn

  • Received Date: January 10, 2020
  • Revised Date: August 17, 2020
  • Published Date: September 29, 2021
    Abstract
  • To use educational resources efficiently and dig out the nature of relations among MOOCs (massive open online courses), a knowledge graph was built for MOOCs on four major platforms:Coursera, EDX, XuetangX, and ICourse. This paper demonstrates the whole process of educational knowledge graph construction for reference. And this knowledge graph, the largest knowledge graph of MOOC resources at present, stores and represents five classes, 11 kinds of relations and 52 779 entities with their corresponding properties, amounting to more than 300 000 triples. Notably, 24 188 concepts are extracted from text attributes of MOOCs and linked them directly with corresponding Wikipedia entries or the closest entries calculated semantically, which provides the normalized representation of knowledge and a more precise description for MOOCs far more than enriching words with explanatory links. Besides, prerequisites discovered by direct extractions are viewed as an essential supplement to augment the connectivity in the knowledge graph. This knowledge graph could be considered as a collection of unified MOOC resources for learners and the abundant data for researchers on MOOC-related applications, such as prerequisites mining.
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