The DOI matches its Crossref record; 9 of the 11 references it cites were found in a registry or an index.
Automatic Construction of Subject Knowledge Graph based on Educational Big Data
Abstract
In this paper, we propose an automatic construction method of subject knowledge graph for educational applications. The subject knowledge graph is constructed based on educational big data by using a bootstrapping strategy to gradually expand knowledge points and connections between them. In this paper two different datasets are used. One is the subject teaching resources such as syllabuses, teaching plans, textbooks and etc., which is used to automatically construct the core of subject knowledge graph so as to reduce the dependence on the manual annotation. Meanwhile the high-quality of subject teaching resources is the guarantee of accuracy of the knowledge graph core. The other dataset is the massive Internet encyclopedia texts, which is used to expand and complete the subject knowledge graph. As to algorithm, this paper utilizes the BERT-BiLSTM-CRF model to automatically identify the subject knowledge points, and then evaluates the relationship between the knowledge points by calculating their semantic similarity, PMI and Normalized Google Distance between them. The experimental results show that BERT-BiLSTM-CRF outperforms the baselines significantly, and the three kinds of relationship evaluation models have achieved good results. Finally, computer science and physics science are taken as examples to construct the subject knowledge graphs successfully, which show the effectiveness of our method.
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“For classification tasks, LLMs-based innovations have shown high performance for simple educational tasks”
From Practical and ethical challenges of large language models in education: A systematic scoping review (2024) · cites this paper as Su & Zhang, 2020
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Cited by
Practical and ethical challenges of large language models in education: A systematic scoping review
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13370 · Cited by 1,435
“…F1 = 0.95) (Fonseca et al., 2020), analysing the sentiment of student feedback (best F1 = 0.94) (Truong et al., 2020), constructing subject knowledge graph from teaching materials (best F1 = 0.94) (Su and Zhang, 2020), and classifying educational forum posts (Sha et al., 2022c) (best F1 = 0.92).”
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Metadata
- Authors
- Su, Y., & Zhang, Y.
- Affiliations
- Wuchang Shouyi University (CN)
- Central China Normal University (CN)
- ORCID
- Zhang, Yong
- Venue
- Proceedings of the 2020 3rd International Conference on Big Data and Education
- Publisher
- ACM
- Published
- 2020-04
- Pages
- 30-36
- OpenAlex
- W3032880579
- Cited by
- 34
- References
- 11
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 12 · 2023: 6 · 2022: 6 · 2021: 2
- FWCI
- 1.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Big Data and Education (ICBDE) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- knowledge graph construction, big data in education, bootstrapping, teaching materials, BERT-BiLSTM-CRF, semantic similarity, pointwise mutual information, normalized Google distance, relation extraction
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