Knowledge Graph-Based Core Concept Identification in Learning Resources

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11341)


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.


  1. 1.
    Boudin, F.: A comparison of centrality measures for graph-based keyphrase extraction. In: IJCNLP (2013)Google Scholar
  2. 2.
    Changuel, S., Labroche, N., Bouchon-Meunier, B.: Resources sequencing using automatic prerequisite-outcome annotation. ACM Trans. Intell. Syst. Technol. 6(1), 6:1–6:30 (2015). Scholar
  3. 3.
    Estivill-Castro, V., Limongelli, C., Lombardi, M., Marani, A.: DAJEE: a dataset of joint educational entities for information retrieval in technology enhanced learning. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016. ACM (2016)Google Scholar
  4. 4.
    Färber, M., Ell, B., Menne, C., Rettinger, A.: A comparative survey of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semant. Web J., pp. 1–26, July 2015.
  5. 5.
    Farhat, R., Jebali, B., Jemni, M.: Ontology based semantic metadata extraction system for learning objects. In: Chen, G., Kumar, V., Kinshuk, Huang, R., Kong, S.C. (eds.) Emerging Issues in Smart Learning, pp. 247–250. Springer, Berlin (2015). Scholar
  6. 6.
    Foster, J.M., Sultan, M.A., Devaul, H., Okoye, I., Sumner, T.: Identifying core concepts in educational resources. In: Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries. JCDL 2012, pp. 35–42. ACM, New York (2012).
  7. 7.
    Grévisse, C., Manrique, R., Mariño, O., Rothkugel, S.: Knowledge graph-based teacher support for learning material authoring. In: Serrano, C.J., Martínez-Santos, J. (eds.) CCC 2018. CCIS, vol. 885, pp. 177–191. Springer, Cham (2018). Scholar
  8. 8.
    Grévisse, C., Manrique, R., Mariño, O., Rothkugel, S.: SoLeMiO: semantic integration of learning material in office. In: Proceedings of E-Learn: World Conference on E-Learning 2018. Association for the Advancement of Computing in Education (AACE) (in press)Google Scholar
  9. 9.
    Ichinose, S., Kobayashi, I., Iwazume, M., Tanaka, K.: Ranking the results of DBpedia retrieval with SPARQL query. In: Kim, W., Ding, Y., Kim, H.-G. (eds.) JIST 2013. LNCS, vol. 8388, pp. 306–319. Springer, Cham (2014). Scholar
  10. 10.
    Jebali, B., Farhat, R.: Ontology-based semantic metadata extraction approach. In: 2013 International Conference on Electrical Engineering and Software Applications, pp. 1–5, March 2013.
  11. 11.
    Krieger, K., Schneider, J., Nywelt, C., Rösner, D.: Creating semantic fingerprints for web documents. In: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics. WIMS 2015, pp. 11:1–11:6 (2015).
  12. 12.
    Limongelli, C., Lombardi, M., Marani, A., Taibi, D.: Enrichment of the dataset of joint educational entities with the web of data. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pp. 528–529 (2017)Google Scholar
  13. 13.
    Manrique, R., Cueto-Ramirez, F., Mariño, O.: Comparing graph similarity measures for semantic representations of documents. In: Serrano, C.J., Martínez-Santos, J. (eds.) CCC 2018. CCIS, vol. 885, pp. 162–176. Springer, Cham (2018). Scholar
  14. 14.
    Manrique, R., Herazo, O., Mariño, O.: Exploring the use of linked open data for user research interest modeling. In: Solano, A., Ordoñez, H. (eds.) CCC 2017. CCIS, vol. 735, pp. 3–16. Springer, Cham (2017). Scholar
  15. 15.
    Manrique, R., Mariño, O.: How does the size of a document affect linked open data user modeling strategies? In: Proceedings of the International Conference on Web Intelligence. WI 2017, pp. 1246–1252. ACM, New York (2017).
  16. 16.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems. I-Semantics 2011, pp. 1–8. ACM, New York (2011)Google Scholar
  17. 17.
    Mirizzi, R., Ragone, A., Di Noia, T., Di Sciascio, E.: Ranking the linked data: the case of DBpedia. In: Benatallah, B., Casati, F., Kappel, G., Rossi, G. (eds.) ICWE 2010. LNCS, vol. 6189, pp. 337–354. Springer, Heidelberg (2010). Scholar
  18. 18.
    Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017). Scholar
  19. 19.
    Piao, G., Breslin, J.G.: Analyzing aggregated semantics-enabled user modeling on Google+ and Twitter for personalized link recommendations. In: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. UMAP 2016, pp. 105–109. ACM, New York (2016).
  20. 20.
    Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)CrossRefGoogle Scholar
  21. 21.
    Roy, D., Sarkar, S., Ghose, S.: Automatic extraction of pedagogic metadata from learning content. Int. J. Artif. Intell. Educ. 18(2), 97–118 (2008)Google Scholar
  22. 22.
    Siehndel, P., Kawase, R., Nunes, B.P., Herder, E.: Towards automatic building of learning pathways. In: Proceedings of the 10th International Conference on Web Information Systems and Technologies, pp. 270–277 (2014).
  23. 23.
    Sultan, M.A., Bethard, S., Sumner, T.: Towards automatic identification of core concepts in educational resources. In: Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries. JCDL 2014, pp. 379–388. IEEE Press, Piscataway (2014)Google Scholar
  24. 24.
    Waitelonis, J., Exeler, C., Sack, H.: Enabled generalized vector space model to improve document retrieval. In: Proceedings of the Third NLP&DBpedia Workshop (NLP & DBpedia 2015) co-located with the 14th International Semantic Web Conference 2015 (ISWC 2015), Bethlehem, Pennsylvania, USA, 11 October 2015, pp. 33–44 (2015).

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Systems and Computing Engineering Department, School of EngineeringUniversidad de los AndesBogotáColombia
  2. 2.Computer Science and Communications Research UnitUniversity of LuxembourgEsch-sur-AlzetteLuxembourg

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