Research on Course Knowledge Point Extraction and Knowledge Graph Construction Based on Large Language Models
DOI: https://doi.org/10.62517/jhet.202615124
Author(s)
Liwei Gao
Affiliation(s)
Raffles University Malaysia, Iskandar, Malaysia
Abstract
Nowadays, artificial intelligence is deeply integrating with education field, and large language models have already become transformative tools for extracting course knowledge points and constructing knowledge graphs. This research adopts advanced semantic analysis, while also conducting rigorous course text preprocessing, and combines key knowledge point identification, hierarchical classification and relationship analysis, thereby establishing an efficient and structured knowledge framework. Furthermore, through entity modeling, relationship extraction, visualization representation and teaching resource integration, this method promotes intelligent management of teaching content and personalized guidance for learners, with empirical evidence showing that knowledge graphs based on large language models not only improve teaching quality, optimize learning paths, but also provide strong support for educational assessment and intelligent recommendation systems, thereby promoting modernization and refinement of teaching practice.
Keywords
Large Language Models; Knowledge Point Extraction; Knowledge Graph Construction; Educational Teaching Assistance
References
[1]Wang, Y., Huang, W., & Jiang, X. (2025). Innovative value of large language models in editorial skill competition question design. Publishing and Printing, (5), 33–43.
[2]Lu, Y., & Li, X. (2025). Knowledge enhancement strategies and application practices of large language models in the field of construction engineering. Construction Enterprise Management, (7), 57–60.
[3]Wang, Z. (2025). Methods for constructing teaching resources for new engineering courses assisted by knowledge graphs and large language models. Higher Engineering Education Research, (1), 40–46, 110.
[4]Wang, Y. (2024). Understanding the meaning and interpretive challenges of knowledge production by large language models: A hermeneutic turn. Nanjing Social Sciences, (10), 82–93.
[5]Wang, X., Yue, W., Wang, H., et al. (2024). Exploring the integration of large language model technologies into database courses. Computer Education, (9), 28–32.
