@inproceedings{0be1ed0b02e845b3b1fe4027d09290f6,
title = "Construction of Disaster Knowledge Graphs to Enhance Disaster Resilience",
abstract = "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.",
keywords = "disaster, knowledge graph, risk analysis",
author = "Seonhyeong Kim and Kwon, {Young Woo}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",
year = "2022",
doi = "10.1109/BigData55660.2022.10021017",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6721--6723",
editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",
booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
address = "United States",
}