WikiCPRL: A Weakly Supervised Approach for Wikipedia Concept Prerequisite Relation Learning
Authors: 
Kui Xiao, Kun Li, Yan Zhang, Xiang Chen, Yuanyuan LouAuthors Info & Claims
Pages 177 - 192
Abstract
Concept prerequisite relations determine the order in which knowledge concept is learned. This kind of concept relations has been used in a variety of educational applications, such as curriculum planning, learning resource sequencing, and reading list generation. Manually annotating prerequisite relations is time-consuming. Besides, data annotated by multiple people is often inconsistent. These factors have led to significant limitations in the use of supervised concept prerequisite learning methods. In this paper, we propose a weakly supervised Wikipedia Concept Prerequisite Relations Learning approach, called WikiCPRL, to identify prerequisite relations between Wikipedia concepts. First of all, we take the title of each Wikipedia article in a domain as a concept, and employ the RefD algorithm to generate weak labels for all the concept pairs, and then build a concept map for the domain. Secondly, a graph attention layer is defined to fuse the context information of each concept in the concept map so as to update their feature representations. Finally, we use the VGAE model to reconstruct the concept map, and then obtain the concept prerequisite graph. Extensive experiments on both English and Chinese datasets demonstrate that the proposed approach can achieve the same performance as several existing supervised learning methods.
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Published In
Oct 2023
535 pages
ISBN:978-981-97-2389-8
DOI:10.1007/978-981-97-2390-4
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 28 April 2024
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