Knowledge-Graph-Supported Indicator Generation for HSE Management System Evaluation Using BERT-BiLSTM-CRF

Abstract

To improve the systematicity and traceability of HSE system evaluation indicators, this study addresses limitations in traditional indicator selection, including strong dependence on expert experience, high subjectivity, overlapping indicator boundaries and unclear source evidence. Based on a first-level indicator framework determined by enterprise experts, HSE standards, system documents and related literature were used as the corpus. A domain-specific entity annotation scheme was designed, covering management measures, responsible actors, evidence information, risk objects, resource support, tools and technologies, and improvement actions. A BIO-annotated dataset was then constructed, and the BERT-BiLSTM-CRF model was employed for domain entity recognition. On this basis, second-level evaluation indicators were generated and screened through knowledge fusion, graph-path retrieval and topic consolidation. The results show that the BERT-BiLSTM-CRF model achieved a precision of 91.2%, a recall of 89.8% and an F1-score of 90.5%. Based on entity recognition and topic consolidation, an HSE system evaluation indicator system comprising 9 first-level indicators and 36 second-level indicators was developed, providing a basis for subsequent indicator weighting and comprehensive evaluation modeling.