[en] 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.
Disciplines :
Computer science
Author, co-author :
Manrique, Rubén; Universidad de los Andes > Systems and Computing Engineering Department
GREVISSE, Christian ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Mariño, Olga; Universidad de los Andes > Systems and Computing Engineering Department
ROTHKUGEL, Steffen ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
yes
Language :
English
Title :
Knowledge Graph-based Core Concept Identification in Learning Resources
Publication date :
December 2018
Event name :
Joint International Semantic Technology Conference (JIST)
Event place :
Awaji City, Japan
Event date :
from 26-11-2018 to 28-11-2018
Main work title :
8th Joint International Conference, JIST 2018, Awaji, Japan, November 26–28, 2018, Proceedings
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)
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. http://www.semantic-web-journal.net/system/files/swj1141.pdf
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). https://doi.org/10.1007/978-3-662-44188-6 34
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). https://doi.org/10.1145/2232817.2232827
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). https://doi.org/10.1007/978-3-319-98998-3 14
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)
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). https://doi.org/10. 1007/978-3-319-06826-8 23
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. https://doi.org/10.1109/ICEESA.2013.6578408
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).https://doi. org/10.1145/2797115.2797132
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)
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). https://doi.org/10.1007/978-3-319-98998-3 13
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). https://doi.org/10.1007/978-3-319-66562-7 1
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). https://doi.org/10. 1145/3106426.3109440
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)
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). https://doi.org/10.1007/978-3-642-13911-6 23
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017). https://doi.org/10.3233/SW-160218
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). https://doi.org/10.1145/2930238.2930278
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)
Roy, D., Sarkar, S., Ghose, S.: Automatic extraction of pedagogic metadata from learning content. Int. J. Artif. Intell. Educ. 18(2), 97–118 (2008)
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). https://doi.org/10. 5220/0004837602700277
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)
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). http://ceur-ws.org/Vol-1581/paper4.pdf
