Reference : Knowledge Graph-based Core Concept Identification in Learning Resources

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/37231

Title :	Knowledge Graph-based Core Concept Identification in Learning Resources
Language :	English
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) >]
Publication date :	Dec-2018
Main document title :	8th Joint International Conference, JIST 2018, Awaji, Japan, November 26–28, 2018, Proceedings
Publisher :	Springer
Collection and collection volume :	LNCS 11341
Peer reviewed :	Yes
Event name :	Joint International Semantic Technology Conference (JIST)
Event date :	from 26-11-2018 to 28-11-2018
Event place (city) :	Awaji City
Event country :	Japan
Abstract :	[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.
Permalink :	http://hdl.handle.net/10993/37231
DOI :	10.1007/978-3-030-04284-4_3

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