Version 1
: Received: 28 October 2023 / Approved: 30 October 2023 / Online: 30 October 2023 (11:02:59 CET)
How to cite:
Cao, L.; Huang, C.; Wang, Q.; Zhu, X. Knowledge Point Entity and Relation Extraction based on Pre-training Model in Education Resources. Preprints2023, 2023101914. https://doi.org/10.20944/preprints202310.1914.v1
Cao, L.; Huang, C.; Wang, Q.; Zhu, X. Knowledge Point Entity and Relation Extraction based on Pre-training Model in Education Resources. Preprints 2023, 2023101914. https://doi.org/10.20944/preprints202310.1914.v1
Cao, L.; Huang, C.; Wang, Q.; Zhu, X. Knowledge Point Entity and Relation Extraction based on Pre-training Model in Education Resources. Preprints2023, 2023101914. https://doi.org/10.20944/preprints202310.1914.v1
APA Style
Cao, L., Huang, C., Wang, Q., & Zhu, X. (2023). Knowledge Point Entity and Relation Extraction based on Pre-training Model in Education Resources. Preprints. https://doi.org/10.20944/preprints202310.1914.v1
Chicago/Turabian Style
Cao, L., Qihao Wang and Xiaoming Zhu. 2023 "Knowledge Point Entity and Relation Extraction based on Pre-training Model in Education Resources" Preprints. https://doi.org/10.20944/preprints202310.1914.v1
Abstract
Based on the teaching material of "Robotics" course, this paper studied the automatic knowledge graph construction, including knowledge points extraction and knowledge point relation extraction. We proposed a new method of extracting the first-level, second-level, and third-level knowledge points as well as their prerequisite relations of knowledge points in the textbook. For the problem of insufficient knowledge of the pre-trained language model in the specific field, methods such as incremental pre-training and optimization of cost functions are employed to integrate subject knowledge into the pre-trained language model, thus improving its effectiveness. To overcome the problem that the traditional method of relationship extraction can not be applied directly to the extraction of teaching materials, a new scheme for knowledge point relationship extraction based on keyword relationship is proposed. The experimental data from textbooks shows that the F1 score of knowledge point extraction reaches 93%, considerably improved compared to the traditional model. Consequently, the knowledge point entity extraction and relationship extraction methods based on the pre-trained model can effectively extract structured information and facilitate the automatic construction of knowledge graphs.
Keywords
knowledge graph construction; knowledge point extraction; pre-trained language model; relationship extraction
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
