Skip to main content
SpringerLink
Log in

Scholar-Course Knowledge Graph Construction Based on Graph Database Storage

  • Conference paper
  • First Online:
Emerging Technologies for Education (SETE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13089))

Included in the following conference series:

Abstract

Knowledge graph is an effective way to model and represent complex linked data, which have attracted broad research in recent years and have been applied in different fields. Considering the data characteristics and development needs of course platform in SCHOLAT, Scholar-Course Knowledge Graph (SCKG) is built with scholars and courses as the core concept and integrated it into the next version of our course platform. The ontology structure of SCKG is constructed first and then extracted knowledge from different data sources by employing D2R technology, web crawlers, etc. so as to add them to SCKG. There are 110,856 entities and 1,674,961 pairs of relationships in total after the construction of SCKG. 13 b-tree indexes and 3 full-text indexes are created on some key properties to speed up the query and we also defined some constraints on SCKG to ensure data consistency.

This work was supported in part by the National Natural Science Foundation of China under Grant U1811263 and Grant 61772211.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.scholat.com/.

  2. 2.

    https://www.xuetangx.com/.

  3. 3.

    https://www.biendata.xyz/competition/chaindream_mooccube_task1/.

  4. 4.

    https://protege.stanford.edu/.

  5. 5.

    http://www.hozo.jp/.

  6. 6.

    https://github.com/d2rq.

  7. 7.

    https://xueshu.baidu.com/.

  8. 8.

    https://github.com/HtmlUnit.

  9. 9.

    https://neo4j.com/.

  10. 10.

    https://github.com/neo4j-labs/neosemantics.

References

  1. Li, J., Hou, L.: Review on knowledge graph research. J. Shanxi Univ. (Nat. Sci. Edn.) 454–459 (2017)

    Google Scholar 

  2. Liu, Q., Yang, L., et al.: Knowledge graph construction techniques. J. Comput. Res. Dev. 53(3), 582 (2016)

    Google Scholar 

  3. Singhal, A.: Introducing the Knowledge Graph: Things, Not Strings, Official Blog (of Google) (2012)

    Google Scholar 

  4. Ji, S., Pan, S.: A survey on knowledge graphs: representation, acquisition and applications. arXiv preprint arXiv:2002.00388 (2020)

  5. Vrandecic, D., Markus, K., et al.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)

    Article  Google Scholar 

  6. Rebele, T., Suchanek, F., Hoffart, J., Biega, J., Kuzey, E., Weikum, G.: YAGO: a multilingual knowledge base from Wikipedia, wordnet, and geonames. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 177–185. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_19

    Chapter  Google Scholar 

  7. Ashburner, M., Catherine, A., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)

    Article  Google Scholar 

  8. Wang, Z., Li, J., et al.: XLore: a large-scale English-Chinese bilingual knowledge graph. In: International Semantic Web Conference (Posters & Demos), vol. 1035 (2013)

    Google Scholar 

  9. Niu, X., Sun, X., Wang, H., Rong, S., Qi, G., Yu, Y.: Zhishi.me - weaving Chinese linking open data. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7032, pp. 205–220. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25093-4_14

    Chapter  Google Scholar 

  10. Luo, X., Liu, L., et al.: AliCoCo: alibaba e-commerce cognitive concept net. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (2020)

    Google Scholar 

  11. Huang, L., Jiang, B., et al.: Survey on deep learning based recommender systems. Chin. J. Comput. 41(7), 1619–1647 (2018)

    MathSciNet  Google Scholar 

  12. Yang, Y., Xu, B., et al.: Accurate and efficient method for constructing domain knowledge graph. J. Softw. 29(10), 2931–2947 (2018)

    MathSciNet  Google Scholar 

  13. Buscaldi, D., Dessì, D., Motta, E., Osborne, F., Reforgiato Recupero, D.: Mining scholarly publications for scientific knowledge graph construction. In: Hitzler, P., et al. (eds.) ESWC 2019. LNCS, vol. 11762, pp. 8–12. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32327-1_2

    Chapter  Google Scholar 

  14. Yu, J., Luo, G., et al.: MOOCCube: a large-scale data repository for NLP applications in MOOCs. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)

    Google Scholar 

  15. Hou, J.: Constructing higher education knowledge graph for MOOC using data mining methods. Beijing University of Posts and Telecommunications (2017)

    Google Scholar 

  16. Huang, C.: Research on the method of extracting course knowledge graph for wisdom education. Harbin Institute of Technology (2020)

    Google Scholar 

  17. Zhao, Z., Han, S., et al.: Architecture of knowledge graph construction techniques. Int. J. Pure Appl. Math. 118(19), 1869–1883 (2018)

    MathSciNet  Google Scholar 

  18. Aidan, H., Michael, C., et al.: Knowledge graphs. arXiv preprint arXiv:2003.02320 (2020)

  19. Yan, J., Wang, C., Cheng, W., Gao, M., Zhou, A.: A retrospective of knowledge graphs. Front. Comp. Sci. 12(1), 55–74 (2018). https://doi.org/10.1007/s11704-016-5228-9

    Article  Google Scholar 

  20. Bizer, C.: D2R map-a database to RDF mapping language (2003)

    Google Scholar 

  21. Bizer, C., Seaborne, A.: D2RQ-treating non-RDF databases as virtual RDF graphs. In: Proceedings of the 3rd International Semantic Web Conference (ISWC2004), Proceedings of ISWC2004, vol. 2004 (2004)

    Google Scholar 

  22. Lin, Y., Zhou, J., et al.: A method of extracting the semi-structured data implication rules. Proc. Comput. Sci. 131, 706–716 (2018)

    Article  Google Scholar 

  23. Goyal, A., Gupta, V.: Recent named entity recognition and classification techniques: a systematic review. Comput. Sci. Rev. 29, 21–43 (2018)

    Article  Google Scholar 

  24. Huang, Z., Xu, W., et al.: Bidirectional LSTM-CRF models for sequence tagging [J/OL]. CoRR, abs/1508.01991 (2015). http://arxiv.org/abs/1508.01991

  25. Lample, G., Ballesteros, M., et al.: Neural architectures for named entity recognition In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 260–270 (2016)

    Google Scholar 

  26. Wang, X., Zou, L., et al.: Research on knowledge graph data management: a survey. J. Softw. 30(7), 2140 (2019)

    Google Scholar 

  27. Fernandes, D., Bernardino, J.: Graph databases comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB. DATA 373–380 (2018)

    Google Scholar 

  28. Baton, J., Van Bruggen, R.: Learning Neo4j 3. x: Effective Data Modeling, Performance Tuning and Data Visualization Techniques in Neo4j. Packt Publishing Ltd. (2017)

    Google Scholar 

  29. Guo, Q., Zhuang, F., et al.: A survey on knowledge graph-based recommender systems. IEEE Trans. Knowl. Data Eng. (2020)

    Google Scholar 

  30. Lal, M.: Neo4j Graph Data Modeling. Packt Publishing Ltd. (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, China

    Dongyang Zheng, Yongxu Long, Zekai Zhou, Wande Chen, Jianguo Li & Yong Tang

Authors
  1. Dongyang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongxu Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zekai Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wande Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianguo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Tang .

Editor information

Editors and Affiliations

  1. Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China

    Weijia Jia

  2. South China Normal University, Guangzhou, China

    Yong Tang

  3. Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, China

    Raymond S. T. Lee

  4. Hochschule Magdeburg-Stendal, Magdeburg, Germany

    Michael Herzog

  5. Beijing Normal University-Hong Kong Baptist University United International College, Zhuzai, China

    Hui Zhang

  6. South China Normal University, Guangzhou, China

    Tianyong Hao

  7. Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China

    Tian Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zheng, D., Long, Y., Zhou, Z., Chen, W., Li, J., Tang, Y. (2021). Scholar-Course Knowledge Graph Construction Based on Graph Database Storage. In: Jia, W., et al. Emerging Technologies for Education. SETE 2021. Lecture Notes in Computer Science(), vol 13089. Springer, Cham. https://doi.org/10.1007/978-3-030-92836-0_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-92836-0_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92835-3

  • Online ISBN: 978-3-030-92836-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation