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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Xiao, K., Li, K., Zhang, Y., Chen, X., Lou, Y. (2024). WikiCPRL: A Weakly Supervised Approach for Wikipedia Concept Prerequisite Relation Learning. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14332. Springer, Singapore. https://doi.org/10.1007/978-981-97-2390-4_13
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DOI: https://doi.org/10.1007/978-981-97-2390-4_13
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