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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.

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.

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.

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.

The purpose of this paper is to investigate the mediating effect of felt obligation for constructive change on the relationship between responsible leadership and organizational citizenship behavior for the environment (OCBE) in a China corporate environment, and this paper also analyze the moderated mediating effect of supervisor-subordinate guanxi on indirect relationship between responsible leadership and OCBE via felt obligation for constructive change.

This paper used 380 employee samples to analyze the relationship between responsible leadership and OCBE. Hierarchical regression analyses and structural equation modeling was adopted to analyze the data.

The authors found that the felt obligation for constructive change plays a fully mediating role between responsible leadership and OCBE. The authors also found a positive interaction between responsible leadership and supervisor-subordinate guanxi on felt obligation for constructive change, and then the indirect effect of responsible leadership on OCBE via felt obligation for constructive change was stronger when employees perceived a high-level supervisor-subordinate guanxi.

When responsible leadership stimulates employees to generate a high sense of constructive change, employees are more likely to engage in OCBE. This study provides evidence for cognitive evaluation theory. This study further demonstrated the importance of establishing high-quality supervisor-subordinate guanxi for responsible leaders and subordinates in China.

In the management practice of the organization, the role of responsible leadership should be strengthened in terms of leadership development and, employee training and promotion, and high-quality supervisor-subordinate guanxi help to promote the effectiveness of responsible leadership.

This paper discusses how and when responsible leadership influences OCBE in a China corporate environment.