Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach

• URL: http://arxiv.org/abs/2509.05393v1
• Date: Fri, 05 Sep 2025 10:37:58 GMT
• Title: Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach
• Authors: Rawaa Alatrash, Mohamed Amine Chatti, Nasha Wibowo, Qurat Ul Ain,
• Abstract summary: We propose an unsupervised method for automatically inferring concept PRs without relying on labeled data.<n>We define ten criteria based on document-based, hyperlink-based, and text-based features, and combine them using a voting algorithm to robustly capture PRs in educational content.
• Score: 1.4081145216286748
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Educational Knowledge Graphs (EduKGs) organize various learning entities and their relationships to support structured and adaptive learning. Prerequisite relationships (PRs) are critical in EduKGs for defining the logical order in which concepts should be learned. However, the current EduKG in the MOOC platform CourseMapper lacks explicit PR links, and manually annotating them is time-consuming and inconsistent. To address this, we propose an unsupervised method for automatically inferring concept PRs without relying on labeled data. We define ten criteria based on document-based, Wikipedia hyperlink-based, graph-based, and text-based features, and combine them using a voting algorithm to robustly capture PRs in educational content. Experiments on benchmark datasets show that our approach achieves higher precision than existing methods while maintaining scalability and adaptability, thus providing reliable support for sequence-aware learning in CourseMapper.

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