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Leveraging Graph Retrieval-Augmented Generation to Support Learners’ Understanding of Knowledge Concepts in MOOCs

A study on using retrieval-augmented generation with educational knowledge graphs and personal knowledge graphs.

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Leveraging Graph Retrieval-Augmented Generation to Support Learners’ Understanding of Knowledge Concepts in MOOCs

By M.F. Abdel-Magied, Mohamed Amine Chatti, Shoeb Joarder, Qurat Ul Ain, Rawaa AlatrashLecture notes in computer science
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The paper proposes a graph retrieval-augmented generation pipeline for MOOCs, leveraging educational knowledge graphs and personal knowledge graphs. It includes two methods: PKG-based question generation and EduKG-based question answering.

The study evaluates the effectiveness of these methods on three MOOCs.

Abstract

The paper proposes a graph retrieval-augmented generation pipeline for MOOCs, leveraging educational knowledge graphs and personal knowledge graphs. It includes two methods: PKG-based question generation and EduKG-based question answering. The study evaluates the effectiveness of these methods on three MOOCs.

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graph retrieval-augmented generationeducational knowledge graphspersonal knowledge graphsmoocsquestion answeringknowledge concept understandingKnowledge GraphsRetrieval & RAGLarge Language ModelsSemantic Interoperability
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