综合减灾知识图谱构建方法研究

防灾减灾、抗灾救灾是人类生存发展的永恒课题，对重大自然灾害的有效监测预警和应急响应已成为世界范围内共同面对的重大挑战，与此同时，大数据作为创新技术和思维革命，使得应急处理理念、行为模式及组织方式发生深刻改变。但由于我国现代应急实践起步晚、受技术限制等原因，数据获取能力与智能化应用能力之间的差距不断加剧，应急能力相对不足。针对当前灾害领域“数据海量，信息爆炸，知识难求”问题，本项目系统研究自然灾害领域的综合减灾知识图谱构建及验证方法。研究内容包括：1）自然灾害时空三域模型与综合减灾知识图谱定义；2）提出数据驱动的增量式知识图谱构建方法；3）构建综合减灾知识图谱智能更新机制；4）以我国最为常见的暴雨-洪涝灾害为例开展实证研究。突破当前“数据丰富、知识匮乏”的局限，从数据中获取知识，实现传统应急信息服务到知识驱动的智能应急知识服务的跃迁，为切实提升自然灾害应急响应能力提供坚实的理论方法基础。

Disaster prevention, mitigation and relief are eternal issues for human survival and development. Effective monitoring, early warning and emergency response to major natural disasters have become common challenges in the world. Big data, as an innovative technology and thinking revolution, is making profound changes in the concept, behavior and organization of emergency response at the same time. The gap between data acquisition ability and intelligent application ability is increasing due to the late start of modern emergency practice and technical constraints in China, which brings about relatively inadequate emergency response ability. To solve the problems of the rapid increase of massive and heterogeneous disaster data and the difficulty of knowledge discovery in data resources, this project systematically studies construction and verification of comprehensive disaster reduction knowledge graph in the field of natural disasters. The research includes: 1) the definitions of spatial-temporal three-domain model and knowledge graph model of natural disasters; 2) a data-driven incremental knowledge graph construction method; 3) an intelligent renewal mechanism of comprehensive disaster reduction knowledge graph; 4) an empirical study of the most common rainstorm-flood disasters in China. Bridging the gap between rich data and lack of knowledge, realizing the transition from traditional emergency information service to intelligent emergency knowledge service driven by knowledge, this project provides solid theoretical and methodological foundations for improving the emergency response capacity of natural disasters.