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Gas knowledge bidirectional encoder representations from transformers model based on knowledge injection
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Xiaoyu LIU, Yufeng ZHUANG**, Xinghao ZHAO, Kefan WANG, Guokai ZHANG
China Safety Science Journal | 2025, 35(3) : 204 - 211
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China Safety Science Journal | 2025, 35(3): 204-211
• Public safety •
Gas knowledge bidirectional encoder representations from transformers model based on knowledge injection
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Xiaoyu LIU, Yufeng ZHUANG**, Xinghao ZHAO, Kefan WANG, Guokai ZHANG
Affiliations
  • School of Intelligent Engineering and Automation，Beijing University of Posts and Telecommunications，Beijing 100876，China
Published: 2025-03-28 doi: 10.16265/j.cnki.issn1003-3033.2025.03.0223
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In order to enhance emergency management in the field of gas pipeline networks，Gas-kBERT model was proposed. The model incorporated data from the gas pipeline network field expanded by Chat Generative Pre-Trained Transformer，(ChatGPT)and Chinese Gas Language Understanding Subject-Predicate-Object(CGLU-Spo) and related corpora were constructed in this field. By altering the model's masking (MASK) mechanism，domain knowledge was successfully injected into the model. Considering the professionalism and specificity of the gas pipeline network field，Gas-kBERT was pre-trained on various scales and contents of corpora and fine-tuned on named entity recognition and classification tasks within this field. Experimental results demonstrated that，compared to the general BERT model，Gas-kBERT exhibited significant performance improvements in F1-score in text mining tasks in the gas pipeline network field. Specifically，in the named entity recognition task，the F1-score was increased by 29.55%，and in the text classification task，the F1-score improvement reached up to 83.33%. This study proves that the Gas-kBERT model performs exceptionally well in text mining tasks in the gas pipeline network field.

gas pipeline networks  /  gas knowledge bidirectional encoder representations from transformers(Gas-kBERT)model  /  natural language processing(NLP)  /  knowledge injection  /  bidirectional encoder representations from transformers (BERT)
Xiaoyu LIU, Yufeng ZHUANG, Xinghao ZHAO, Kefan WANG, Guokai ZHANG. Gas knowledge bidirectional encoder representations from transformers model based on knowledge injection[J]. China Safety Science Journal, 2025 , 35 (3) : 204 -211 . DOI: 10.16265/j.cnki.issn1003-3033.2025.03.0223
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Year 2025 volume 35 Issue 3
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doi: 10.16265/j.cnki.issn1003-3033.2025.03.0223
  • Receive Date：2024-10-14
  • Online Date：2025-07-05
  • Published：2025-03-28
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  • Received：2024-10-14
  • Revised：2024-12-18
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    School of Intelligent Engineering and Automation，Beijing University of Posts and Telecommunications，Beijing 100876，China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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