Construction of Disaster Knowledge Graphs to Enhance Disaster Resilience

As a result of the recent surge in disaster-related data, numerous studies have been conducted to deal with the massive amount of data. In the meantime, the issue of managing data in various formats and representing their relevance is being raised. In this paper, we present a disaster knowledge graph to analyze the impact of a disaster and predict how much effort it will take to recover from the disaster. To that end, we define the structure of a disaster knowledge graph containing data collected from sensors, social networks, web, and risk analysis results. To extract meaningful information from structured and unstructured data, we use a risk analysis platform that can compute hazard values in accordance with various hazard models. Then, we store automatically graphs into a graph database as a form of a time-series data. Therefore, it will be possible to predict the progress of a complex disaster that can occur in a chain using a series of disaster knowledge graphs.