Knowledge Graph of Urban Firefighting with Rule-Based Entity Extraction

There is little research on entity extraction in constructing the knowledge graphs for urban firefighting. In this paper, we propose a rule-based entity extraction method for this field. The Precision of the experiment is 85.25%, while the Recall is 83.58%. In addition, we establish the relationships between entities in urban firefighting in advance with the experience of domain experts. Through the above two steps, we have initially established a knowledge graph in the field of urban firefighting, which including 13 types of entities and 12 types of relationships. This study will provide reference for the construction of knowledge graphs in the field of urban firefighting.