Construction of a Semantic Network for International Chinese Language Education Based on Knowledge Graph Technology and Optimization of Its Teaching Resources
et
23 sept. 2025
À propos de cet article
Publié en ligne: 23 sept. 2025
Reçu: 25 janv. 2024
Accepté: 30 avr. 2025
DOI: https://doi.org/10.2478/amns-2025-1112
Mots clés
© 2025 Xiaoyun Han et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
Figure 2.
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 |