Abstract
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.
This work was supported by the Northwest Minzu University Project for Introduced Talents under Grant No. Z20062 and Fundamental scientific research expenses for central colleges and universities under Grant No.31920210133.
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Notes
- 1.
Website of OpenKG : http://www.openkg.cn/home
Website of CEC data set: https://github.com/daselab/CEC-Corpus/tree/master/CEC/%E7%81%AB%E7%81%BE
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Wang, X., Nady, S., Zhang, Z., Zhang, M., Wang, J. (2023). Knowledge Graph of Urban Firefighting with Rule-Based Entity Extraction. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_15
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DOI: https://doi.org/10.1007/978-3-031-34204-2_15
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