Generating risk response measures for subway construction by fusion of knowledge and deep learning

This study aims to propose a method to generate response measures for subway construction risks. The HowNet semantic theoretical system was selected and incorporated the knowledge of subway construction risk management to develop the semantic knowledge base. Then, a risk response generation algorithm was designed based on Knowledge-enabled Bidirectional Encoder Representation from Transformers (K-BERT) to fuse knowledge and deep learning (DL). Meanwhile, the BERT in the original K-BERT model was replaced by the Masked Language Model (MLM) as correction BERT (MacBERT) pre-training model suitable for Chinese natural language processing (NLP) tasks. The data-driven and knowledge-driven approaches were combined for better performance of DL. The pre-training model based on MacBERT was selected to undertake the comparative experiment of fusing knowledge and DL. The results showed that the proposed algorithm with the K-BERT fusion of knowledge and DL had better performance than the MacBERT-based model without the knowledge base.