Metro Emergency Knowledge Extraction and Knowledge Reasoning Based on BiLSTM-CRF

Abstract:

To address issues such as the unclear sequence of procedures and ambiguity in the personnel responsible for executing the emergency response procedures in text-based metro emergency response processes， this paper proposes a knowledge extraction and knowledge reasoning method for metro emergency response procedures based on knowledge graph of bidirectional long short-term memory- conditional random field （BiLSTM-CRF）. First， the BiLSTM-CRF method is used to identify the named entity of the text data of the metro emergency response process， and complete the knowledge extraction of the text data. Then， the TransD model is selected to conduct knowledge inference on the identified entity data， thereby completing the construction of a knowledge graph with entities and attribute pairs as nodes and relational pairs as edges. Finally， the Neo4j graph database is used to visualize and analyze the knowledge graph of metro emergency response process. The research results show that the precision， recall， and F1 value of the knowledge extraction model based on BiLSTM-CRF have all reached more than 90%， and the accuracy of the inference results of the TransD model based on BiLSTM-CRF has increased by 22.92%， ensuring the accuracy of knowledge graph construction and providing decision support for subway emergency management.

Key words:metro emergency response;knowledge graph;bidirectional long short-term memory- conditional random field (BiLSTM-CRF);TransD model;knowledge extraction