Textual knowledge entity extraction of hidden dangers in coal mine accidents based on probabilistic fusion algorithm

Given the unstructured nature of text data related to hidden dangers in coal mine accidents, extracting latent knowledge is crucial for constructing a knowledge graph of hidden dangers in coal mine accidents. This study proposes annotation types for knowledge entities to describe hidden dangers in coal mine accidents by analyzing the characteristics and latent information in the texts of hidden dangers based on their propagation patterns. Using the Brat annotation tool, we annotated the text data related to hidden dangers of coal mine accidents to construct a dataset for knowledge extraction model. We proposes a BERT-IDCNN-CRF model based on dynamic fusion and introduced a probabilistic fusion algorithm based on Newton's law of cooling. The results indicate that with the incorporation of the probabilistic fusion algorithm, the dynamically weighted BERT-IDCNN-CRF model achieved the best performance in the task of knowledge entity extraction from hidden danger texts. Its precision, recallrate, and F1-score improved by 8.93%, 5.28%, and 7.51%, respectively, significantly enhancing the model's prediction accuracy and stability, while demonstrating excellent adaptability.