Abstract
This article argues that digital educational content should be structured as knowledge graphs (KGs). Unlike traditional repositories such as Moodle, a KG offers a more flexible representation of the relationships between concepts, facilitating intuitive navigation and discovery of connections. In addition, it integrates effectively with Large Language Models, enhancing personalized explanations, answers, and recommendations. This article studies different proposals based on semantics and knowledge modelling to determine the most appropriate ways to strengthen intelligent educational technologies.- Anthology ID:
- 2024.kallm-1.9
- Volume:
- Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Russa Biswas, Lucie-Aimée Kaffee, Oshin Agarwal, Pasquale Minervini, Sameer Singh, Gerard de Melo
- Venues:
- KaLLM | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 85–91
- Language:
- URL:
- https://aclanthology.org/2024.kallm-1.9
- DOI:
- Cite (ACL):
- Miquel Canal-Esteve and Yoan Gutierrez. 2024. Educational Material to Knowledge Graph Conversion: A Methodology to Enhance Digital Education. In Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024), pages 85–91, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Educational Material to Knowledge Graph Conversion: A Methodology to Enhance Digital Education (Canal-Esteve & Gutierrez, KaLLM-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.kallm-1.9.pdf