Searching Temporal Knowledge Graphs to Understand the Impacts of Disasters

초록

Due to the increasing number of disasters, studies on responding to disasters using AI and big data technologies have received much attention. However, the diverse data collected during a disaster makes it difficult to utilize such data to understand disaster situations. Furthermore, state-of-the-art technologies are only limited to post-analysis disasters. To that end, in this paper, we first collect disaster data and generate a time-series temporal knowledge graph to establish relationships between different data types. Next, we discuss approaches to identifying critical keywords and analyzing disaster situations through graph exploration in real-time. As case studies, we select blackouts, typhoons, fires, and earthquakes to apply our approach, and the experimental results show that we can acquire disaster-specific information. Finally, we discuss how our approach can be applied to the government's disaster management system or policies, thereby increasing the overall understanding of disasters.

키워드