Advertisement

Building a Large-Scale Knowledge Graph for Elementary Education in China

  • Wei Zheng
  • Zhichun WangEmail author
  • Mingchen Sun
  • Yanrong Wu
  • Kaiman Li
Conference paper
  • 59 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1157)

Abstract

With the penetration of information technology into all areas of society, Internet-assisted education has become an important opportunity for current educational reform. In order to better assist in teaching and learning, help students deepen their understanding and absorption of knowledge. We build a knowledge graph for elementary education, firstly, we define elementary education ontology, divide the knowledge graph into three sub-graphs. Then extracting concept instance and relation instance form textbook and existing knowledge base based on unsupervised method. In addition, we have acquired four different learning resources to assist in learning. At last, the results show that the procedure we proposed is scientific and efficient.

Keywords

Elementary education Education ontology Knowledge discovery 

Notes

Acknowledgment

The work is supported by the National Key R&D Program of China (No. 2017YFB1402105).

References

  1. 1.
    Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)Google Scholar
  2. 2.
    Anderson, L.W., Sosniak, L.A.: Bloom’s Taxonomy. University of Chicago Press, Chicago (1994)Google Scholar
  3. 3.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  4. 4.
    Brank, J., Leban, G., Grobelnik, M.: Annotating documents with relevant Wikipedia concepts. In: Proceedings of SiKDD (2017)Google Scholar
  5. 5.
    Bucos, M., Dragulescu, B., Veltan, M.: Designing a semantic web ontology for e-learning in higher education. In: International Symposium on Electronics and Telecommunications (2011)Google Scholar
  6. 6.
    Chen, P., Lu, Y., Zheng, V.W., Chen, X., Yang, B.: Knowedu: a system to construct knowledge graph for education. IEEE Access 6, 31553–31563 (2018)CrossRefGoogle Scholar
  7. 7.
    Chung, H., Kim, J.: An ontological approach for semantic modeling of curriculum and syllabus in higher education. Int. J. Inf. Educ. Technol. 6(5), 365 (2016)Google Scholar
  8. 8.
    Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
  9. 9.
    Duan, H., Zheng, Y., Shi, L., Jin, C., Zeng, H., Liu, J.: DKG: an expanded knowledge base for online course. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10179, pp. 376–386. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55705-2_30CrossRefGoogle Scholar
  10. 10.
    Hu, J., Li, Z., Xu, B.: An approach of ontology based knowledge base construction for Chinese k12 education. In: 2016 First International Conference on Multimedia and Image Processing (ICMIP), pp. 83–88. IEEE (2016)Google Scholar
  11. 11.
    Liang, C., Wu, Z., Huang, W., Giles, C.L.: Measuring prerequisite relations among concepts. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1668–1674 (2015)Google Scholar
  12. 12.
    Liang, J., Xiao, Y., Zhang, Y., Hwang, S.W., Wang, H.: Graph-based wrong ISA relation detection in a large-scale lexical taxonomy. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar
  13. 13.
    Okoli, C., Mehdi, M., Mesgari, M., Nielsen, F.Å., Lanamäki, A.: Wikipedia in the eyes of its beholders: a systematic review of scholarly research on Wikipedia readers and readership. J. Assoc. Inf. Sci. Technol. 65(12), 2381–2403 (2014)CrossRefGoogle Scholar
  14. 14.
    Pappano, L.: The year of the MOOC. New York Times 2(12), 2012 (2012)Google Scholar
  15. 15.
    Rush, E.K., Tracy, S.J.: Wikipedia as public scholarship: communicating our impact online. J. Appl. Commun. Res. 38(3), 309–315 (2010)CrossRefGoogle Scholar
  16. 16.
    Schweitzer, N.J.: Wikipedia and psychology: coverage of concepts and its use by undergraduate students. Teach. Psychol. 35(2), 81–85 (2008) CrossRefGoogle Scholar
  17. 17.
    Singhal, A.: Introducing the knowledge graph: things, not strings. Off. Google Blog 5 (2012)Google Scholar
  18. 18.
    Wang, S., et al.: Using prerequisites to extract concept maps from textbooks. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 317–326. ACM (2016)Google Scholar
  19. 19.
    Wang, Z., Wang, H., Wen, J.R., Xiao, Y.: An inference approach to basic level of categorization. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 653–662. ACM (2015)Google Scholar
  20. 20.
    Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 481–492. ACM (2012)Google Scholar
  21. 21.
    Zheng, Y., Liu, R., Hou, J.: The construction of high educational knowledge graph based on MOOC. In: 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 260–263. IEEE (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Wei Zheng
    • 1
  • Zhichun Wang
    • 1
    Email author
  • Mingchen Sun
    • 1
  • Yanrong Wu
    • 1
  • Kaiman Li
    • 1
  1. 1.School of Artificial IntelligenceBeijing Normal UniversityBeijingPeople’s Republic of China

Personalised recommendations