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Knowledge Graph-Based Core Concept Identification in Learning Resources

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Semantic Technology (JIST 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11341))

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Abstract

The automatic identification of core concepts addressed by a learning resource is an important task in favor of organizing content for educational purposes and for the next generation of learner support systems. We present a set of strategies for core concept identification on the basis of a semantic representation built using the open and available knowledge in the so-called Knowledge Graphs (KGs). Different unsupervised weighting strategies, as well as a supervised method that operates on the semantic representation, were implemented for core concept identification. In order to test the effectiveness of the proposed strategies, a human-expert annotated dataset of 96 learning resources extracted from MOOCs was built. In our experiments, we show the capacity of the semantic representation for the core-concept identification task as well as the superiority of the supervised method.

This work was partially supported by COLCIENCIAS PhD scholarship (Call 647-2014).

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Notes

  1. 1.

    https://wiki.dbpedia.org.

  2. 2.

    https://yago-knowledge.org.

  3. 3.

    https://www.wikidata.org/wiki/Wikidata:Main_Page.

  4. 4.

    https://github.com/dbpedia-spotlight/dbpedia-spotlight.

  5. 5.

    https://aylien.com.

  6. 6.

    https://www.ibm.com/watson/alchemy-api.html.

  7. 7.

    http://babelfy.org.

  8. 8.

    Source: https://en.wikipedia.org/wiki/Class_(computer_programming).

  9. 9.

    In this example, DBpedia was used as KG and DBpedia Spotlight as annotation service.

  10. 10.

    https://www.w3.org/TR/sparql11-property-paths/.

  11. 11.

    https://www.w3.org/TR/rdf-sparql-query/.

  12. 12.

    https://networkx.github.io.

  13. 13.

    https://github.com/ceteri/pytextrank.

  14. 14.

    https://github.com/Ruframapi/CCI.

  15. 15.

    https://github.com/coursera-dl.

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Authors and Affiliations

  1. Systems and Computing Engineering Department, School of Engineering, Universidad de los Andes, Bogotá, Colombia

    Rubén Manrique & Olga Mariño

  2. Computer Science and Communications Research Unit, University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Christian Grévisse & Steffen Rothkugel

Authors
  1. Rubén Manrique
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  2. Christian Grévisse
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  3. Olga Mariño
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  4. Steffen Rothkugel
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Corresponding author

Correspondence to Rubén Manrique .

Editor information

Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Ryutaro Ichise

  2. Accenture Labs, Dublin, Ireland

    Freddy Lecue

  3. Japan Science and Technology Agency, Tokyo, Japan

    Takahiro Kawamura

  4. Peking University, Beijing, China

    Dongyan Zhao

  5. Imperial College London, London, UK

    Stephen Muggleton

  6. Osaka University, Osaka, Japan

    Kouji Kozaki

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Manrique, R., Grévisse, C., Mariño, O., Rothkugel, S. (2018). Knowledge Graph-Based Core Concept Identification in Learning Resources. In: Ichise, R., Lecue, F., Kawamura, T., Zhao, D., Muggleton, S., Kozaki, K. (eds) Semantic Technology. JIST 2018. Lecture Notes in Computer Science(), vol 11341. Springer, Cham. https://doi.org/10.1007/978-3-030-04284-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-04284-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04283-7

  • Online ISBN: 978-3-030-04284-4

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