Ontology-Based Knowledge Modeling of Muli-factors for Severe Weather Risks in Snow Sports
Shuangfeng Wei, Xiaobo Sun, Shaobo Zhong · Atlantis Highlights in Computer Sciences/Atlantis highlights in computer sciences · 2022
With the frequent occurrence of severe weather events in winter snow sports, it is important to ensure the agility of response to meteorological emergencies and the intelligence of decision making.To solve the semantic heterogeneity of risk information and insufficient knowledge representation related to severe weather in snow sports, we proposed a knowledge modeling approach driven by ontology to integrate multi-level meteorological risk elements, and constructed a relatively complete knowledge model of severe weather risk.This study found that the unified expression of decentralized concepts and semantic relations within the domain improved the current normalized description of hazard factors and risk emergency for severe weather events in snow sports, providing theoretical support for meteorological risk prediction and emergency response for the upcoming 2022 Beijing Winter Olympics.