Cost-Factor Recognition and Recommendation in Open-Pit Coal Mining via BERT-BiLSTM-CRF and Knowledge Graphs
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| 发表在: | Symmetry vol. 17, no. 11 (2025), p. 1834-1862 |
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MDPI AG
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| 在线阅读: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 100 | 1 | |a Sun, Jiayi |u School of Geology and Mining Engineering, Xinjiang University, Urumqi 830047, China; 107552304865@stu.xju.edu.cn (J.S.); hdbplpf@163.com (P.L.); | |
| 245 | 1 | |a Cost-Factor Recognition and Recommendation in Open-Pit Coal Mining via BERT-BiLSTM-CRF and Knowledge Graphs | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Complex associations among production cost factors, multi-source cost information silos, and opaque transmission mechanisms of hidden costs in open-pit coal mining were addressed. The production process—including drilling, blasting, excavation, transportation, and dumping—was taken as the application context. A corpus of 103 open-pit coal mining standards and related research documents was constructed. Eleven entity types and twelve relationship types were defined. Dynamic word vectors were obtained through transformer (BERT) pre-training. The optimal entity tag sequence was labeled using a bidirectional long short-term memory–conditional random field (BiLSTM–CRF) 9 model. A total of 3995 entities and 6035 relationships were identified, forming a symmetry-aware knowledge graph for open-pit coal mining costs based on the BERT–BiLSTM–CRF model. The results showed that, among nine entity types, including Parameters, the F1-scores all exceeded 60%, indicating more accurate entity recognition compared to conventional methods. Knowledge embedding was performed using the TransH inference algorithm, which outperformed traditional models in all reasoning metrics, with a Hits@10 of 0.636. This verifies its strong capability in capturing complex causal paths among cost factors, making it suitable for practical cost optimization. On this basis, a symmetry-aware BERT–BiLSTM–CRF knowledge graph of open-pit coal mining costs was constructed. Knowledge embedding was then performed with the TransH inference algorithm, and latent relationships among cost factors were mined. Finally, a knowledge-graph-based cost factor identification system was developed. The system lists, for each cost item, the influencing factors and their importance ranking, analyzes variations in relevant factors, and provides decision support. | |
| 653 | |a Coal mining | ||
| 653 | |a Machine learning | ||
| 653 | |a Construction accidents & safety | ||
| 653 | |a Graphs | ||
| 653 | |a Conditional random fields | ||
| 653 | |a Costs | ||
| 653 | |a Discriminant analysis | ||
| 653 | |a Recognition | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Inference | ||
| 653 | |a Pits (excavations) | ||
| 653 | |a Dumping | ||
| 653 | |a Algorithms | ||
| 653 | |a Silos | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Symmetry | ||
| 653 | |a Embedding | ||
| 653 | |a Production costs | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Li Pingfeng |u School of Geology and Mining Engineering, Xinjiang University, Urumqi 830047, China; 107552304865@stu.xju.edu.cn (J.S.); hdbplpf@163.com (P.L.); | |
| 700 | 1 | |a Guan Weiming |u School of Geology and Mining Engineering, Xinjiang University, Urumqi 830047, China; 107552304865@stu.xju.edu.cn (J.S.); hdbplpf@163.com (P.L.); | |
| 700 | 1 | |a Cui Xuejiao |u School of Management, Hunan University of Information Technology, Changsha 110819, China | |
| 700 | 1 | |a Wang, Haosen |u School of Geology and Mining Engineering, Xinjiang University, Urumqi 830047, China; 107552304865@stu.xju.edu.cn (J.S.); hdbplpf@163.com (P.L.); | |
| 700 | 1 | |a Xie Shoudong |u School of Geology and Mining Engineering, Xinjiang University, Urumqi 830047, China; 107552304865@stu.xju.edu.cn (J.S.); hdbplpf@163.com (P.L.); | |
| 773 | 0 | |t Symmetry |g vol. 17, no. 11 (2025), p. 1834-1862 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275564568/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
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