[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
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