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

arXiv: https://arxiv.org/abs/2509.05393 · HTML: https://mlsanity.com/paper/2509.05393/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations](https://mlsanity.com/paper/1801.06481/) (2018)
- [Modeling and Leveraging Prerequisite Context in Recommendation](https://mlsanity.com/paper/2209.11471/) (2022)

## Built on by (most cited first, 3 of 3)

- [Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/2607.19253/) (2026)
- [From SQL Errors to Concept Gaps: An AI-Powered Knowledge Graph Analytics Platform for Personalized Feedback](https://mlsanity.com/paper/2608.03118/) (2026)
- [Evaluating and Improving Pedagogical Fit in LLM-Based AI Tutors with the Pedagogical Suitability Index](https://mlsanity.com/paper/2608.05411/) (2026)

## Abstract (arXiv)

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.