关键词: 农业气象灾害, 知识图谱, 知识抽取, 知识融合

Abstract:

Efficient utilization of massive heterogeneous data is the key factor to enhance the intelligence of agricultural disaster management. Therefore, it is important to explore techniques for constructing multi-source heterogeneous agricultural meteorological disaster knowledge graphs for dynamic monitoring of agricultural meteorological disasters and intelligent management decision making. This paper analyzed the data sources, types, and characteristics required for knowledge graph construction in the agricultural meteorological disaster domain through literature studies and proposed a framework for knowledge graph construction that combined top-down and bottom-up approaches. The paper also examined key techniques and the current application status of knowledge graph construction from the perspective of schema layer construction, entity extraction, relation extraction, and knowledge fusion. In addition, it explored the applications of agricultural meteorological disaster knowledge graphs in the fields of monitoring and early warning, risk assessment, intelligent service, and decision support. It summarized the challenges of constructing agricultural meteorological disaster knowledge graphs and discussed the future development directions. Integrating information from the different modalities could make knowledge graph more comprehensive and accurate in describing and expressing the knowledge and information in the field of agricultural meteorological disasters, which could help to mitigate the losses caused by agricultural meteorological disasters and improve the accuracy and efficiency of decision-making. In the future, agricultural meteorological disaster knowledge graph will be constructed by incorporating large language models, advanced knowledge extraction methods to achieve complex entity and relationship extraction, and multi modal data. Further research is needed to advance the technical study of agricultural meteorological disaster knowledge graph.

Key words: Agricultural meteorological disasters, Knowledge graph, Knowledge extraction, Knowledge fusion