Application of Graph Convolutional Network in the Construction of Knowledge Graph for Higher Mathematics Teaching

Artigo2023

Ruixue Yuan ORCID; Hongming Li ORCID; Zhenyu Sun ORCID; Huiwen Zhang ORCID

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Resumo

In this study, we first perform data preprocessing using higher mathematics textbooks and network resources to complete the construction of the dataset extracted from the knowledge of this mathematical discipline in order to study the application of a graph neural network in the knowledge graph (KG) of higher mathematics.Then, the graph convolutional network (GCN) and attention mechanism are introduced, and the relationship extraction model based on attention-guided GCN and the text classification model based on GCN are established, whereby the automatic extraction of higher mathematical knowledge can be realized.Finally, sensors are used to collect data from students' classroom responses, the data layer construction of a subject KG is realized, and the database is used to realize the storage visualization of triples.The results show that on the basis of the text classification model of GCN, higher mathematical knowledge classification is set, providing a reference example for the application of GCN in higher education.The constructed higher mathematics atlas has a total of 2580 triples, which can be used on the higher mathematics visual query platform.The self-built recognizer recognition test set obtains a P-value of 89.16% in the higher mathematics theorem law entity and a P-value of 97.73% in the test question of higher mathematics.Therefore, the GCN model established here can be effective in the construction of higher mathematical KGs.

Dados da obra

Tipo: Artigo
Periódico: Sensors and Materials, v. 35, n. 12, p. 4269
Editora: MYU K.K.
Ano: 2023 (2023-12-22)
Idioma: en
Palavras-chave: graph convolutional networks; knowledge graph construction; university mathematics education; attention mechanism; relation extraction; text classification; automatic knowledge extraction; mathematics textbooks
DOI: 10.18494/sam4340
ISSN: 0914-4935, 2435-0869
Licença: cc-by
Acesso: Download disponível
Fontes: OpenAlex
Na Biblioteca desde: 01/10/2026 · verificada em 03/10/2026

Como citar

ABNT
YUAN, Ruixue et al.. Application of Graph Convolutional Network in the Construction of Knowledge Graph for Higher Mathematics Teaching. Sensors and Materials, v. 35, n. 12, p. 4269, 2023. DOI: https://doi.org/10.18494/sam4340.

APA

Yuan, R., Li, H., Sun, Z., & Zhang, H. (2023). Application of Graph Convolutional Network in the Construction of Knowledge Graph for Higher Mathematics Teaching. Sensors and Materials, 35(12), 4269. https://doi.org/10.18494/sam4340

BibTeX

@article{yuan2023,
  author = {Yuan, Ruixue and Li, Hongming and Sun, Zhenyu and Zhang, Huiwen},
  title = {Application of Graph Convolutional Network in the Construction of Knowledge Graph for Higher Mathematics Teaching},
  year = {2023},
  journal = {Sensors and Materials},
  volume = {35},
  number = {12},
  pages = {4269},
  publisher = {MYU K.K.},
  doi = {10.18494/sam4340},
  language = {en}
}

RIS

TY  - JOUR
AU  - Yuan, Ruixue
AU  - Li, Hongming
AU  - Sun, Zhenyu
AU  - Zhang, Huiwen
TI  - Application of Graph Convolutional Network in the Construction of Knowledge Graph for Higher Mathematics Teaching
PY  - 2023
JO  - Sensors and Materials
VL  - 35
IS  - 12
SP  - 4269
DO  - 10.18494/sam4340
LA  - en
ER  -

Referência gerada automaticamente a partir dos metadados das fontes; confira com o documento original. Baixar BibTeX · Baixar RIS