Xu, N; Liang, Y; Guo, C; Meng, B; Zhou, X; Hu, Y and Zhang, B (2025) Entity recognition in the field of coal mine construction safety based on a pre-training language model. Engineering, Construction and Architectural Management, 32(4), pp. 2590-2613. ISSN 0969-9988
Abstract
Purpose: Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts. Design/methodology/approach: Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity's optimal tag sequence. Findings: Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods. Originality/value: This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | entity recognition; knowledge graphs; pre-training language model; safety management; standard specifications |
| Index terms: | security management, implementation, platform, efficiency, methodology, safety standards, safety management, construction safety, specification, accuracy, experiment |
| Subjects: | data collection methods, professional development, contractual condition, performance management, environmental health, occupational health and safety management, contractual arrangements, digital design, health safety and environment, research methods |
| Topics: | Quality Management, Sustainability, Procurement, Health and Safety, Contract Administration, Digital Applications, Information Management, Research Practice |
| Descriptive scope: | 4 PCTE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here