Knowledge Graph-based Core Concept Identification in Learning Resources
English
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) >]
Dec-2018
8th Joint International Conference, JIST 2018, Awaji, Japan, November 26–28, 2018, Proceedings
Springer
LNCS 11341
Yes
Joint International Semantic Technology Conference (JIST)
from 26-11-2018 to 28-11-2018
Awaji City
Japan
[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.