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Construction of a Semantic Network for International Chinese Language Education Based on Knowledge Graph Technology and Optimization of Its Teaching Resources

Xiaoyun Han

Xiaoyun Han

College of Humanities Law and Foreign Languages, Taiyuan University of TechnologyJinzhong, China
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Han, Xiaoyun
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Yingping Liang

Yingping Liang

Qiushi College (Zongfu College), Taiyuan University of TechnologyJinzhong, China
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Liang, Yingping
  
23. Sept. 2025
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Online veröffentlicht: 23. Sept. 2025
Eingereicht: 25. Jan. 2024
Akzeptiert: 30. Apr. 2025
DOI: https://doi.org/10.2478/amns-2025-1112
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© 2025 Xiaoyun Han et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Flow chart of construction and application of terminology knowledge graph
Flow chart of construction and application of terminology knowledge graph

Figure 2.

Cross task model comparison results
Cross task model comparison results

Statistical results of the knowledge map

Categories Building of knowledge map Node number Side number Triad Apogee Network density(%) Condensed subgroup
Chinese Experiment 1 3894 2431 4445 65 0.17 418
Experiment 2 6124 2946 6124 98 0.18 551
Mathematics and physics Experiment 1 4308 1838 8431 205 0.31 244
Experiment 2 8643 2679 8643 221 0.39 429
Chemistry and biology Experiment 1 7612 2520 5349 52 0.25 267
Experiment 2 6617 2637 5617 123 0.29 441
History and geography Experiment 1 4981 1832 5208 142 0.36 154
Experiment 2 5741 2269 5741 159 0.47 323
Politics Experiment 1 6338 2698 4171 53 0.22 307
Experiment 2 6967 3020 4439 147 0.39 568

Knowledge semantic network entity extraction results

Categories Physical extraction method Document number Complex concept Entity number Mean length Accuracy rate(%)
Chinese Ansj 1352 2339 2177 2.35 91.58
Ours 3382 3252 2.56 95.45
Mathematics and physics Ansj 3512 2991 2854 2.41 86.44
Ours 3327 3178 2.85 91.05
Chemistry and biology Ansj 1315 2875 2280 2.55 90.63
Ours 3941 3100 3.93 96.32
History and geography Ansj 1293 2286 1859 2.64 92.15
Ours 3060 3255 2.87 97.66
Politics Ansj 846 1740 1381 2.45 94.33
Ours 3342 3094 2.76 97.72

Comparison results of different models

Model CTec2018 CTec2020
Accuracy Recall F1 Accuracy Recall F1
CNN-BiLSTM-CRF 66.17 68.02 67.08 79.93 78.69 79.33
BERT-CRF 69.07 74.52 71.74 83.25 83.50 83.37
AdapCAN-Bert-CRF 69.82 74.52 72.08 85.06 83.13 84.03
VisualBERT 68.77 71.32 70.02 83.99 84.32 84.65
OCSGA 74.64 71.14 72.85 -- -- --
UMT 71.60 75.16 73.34 85.21 85.27 85.24
UMGF 74.41 75.14 74.78 86.47 84.43 85.44
HVPNet 73.80 76.75 75.25 85.77 87.86 86.82
Ours 75.79 76.91 76.34 87.42 88.03 87.23
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