Intelligent Technology Assessment of High-Speed Railway Based on Knowledge Graphs

Abstract

This paper introduces an intelligent technology assessment framework for high-speed railways based on a knowledge graph approach. We employ rule-based knowledge extraction algorithms and a Bert-BiLSTM-CRF model for entity extraction from technical texts. Subsequently, we establish relationships among various entities, constructing a knowledge graph specific to high-peed railways. The knowledge graph is stored in a Neo4j graph database in triple format. Furthermore, we establish a comprehensive evaluation metric system, integrating knowledge graph insights to assess the utility of enabling technologies. We employ the CRITIC weighting method to calculate the value assessment results for target technologies. Simulation results indicate that the training results for word segmentation and sentence splitting are favorable, and the Bert-BiLSTM-CRF model achieves convergence in accuracy, recall, and F1 score after 30 iterations. The technical assessment results in this paper align closely with the actual technological value assessment.