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
主要作者: Sun, Jiayi
其他作者: Li Pingfeng, Guan Weiming, Cui Xuejiao, Wang, Haosen, Xie Shoudong
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MDPI AG
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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 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3275564568/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
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