A Nuclear Power Equipment Failure Knowledge Graph Construction Method Based on the CHINESE-ROBERTa-BiLSTM-CRF Model

Abstract

This paper proposes a method for constructing a knowledge graph for nuclear power equipment failures based on the CHINESE-RoBERTa-BiLSTM-CRF model, whose goal is to automate the extraction and structured utilization of nuclear power equipment failure knowledge and provide decision support for nuclear power equipment debugging work. First, the method involves constructing annotated datasets for Named Entity Recognition (NER) and Relation Extraction (RE); Second, the CHINESE-RoBERTa-BiLSTM-CRF model is constructed. This model employs Whole Word Masking (WWM) to capture deep contextual semantics. It also utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network to enhance sequence features and implements a Conditional Random Field (CRF) layer for optimized boundary recognition. Finally, the method classifies relationships between entity pairs. The resulting triples are stored in a Neo4j graph database to complete the knowledge graph construction. The nuclear power equipment fault knowledge graph constructed by this method is validated using model comparisons and case verification. Experimental results demonstrate that the NER model proposed in this paper achieves an F1 score of 94.9%, significantly outperforming the baseline model. Furthermore, practical verification confirms that the knowledge graph can accurately identify faults and provide decision support for on-site anomalies.