TY - GEN
T1 - Applying GNN Models for Diverse Disaster Detection using Temporal Knowledge Graphs
AU - Kim, Seonhyeong
AU - Kwon, Young Woo
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - As unexpected disasters increase, the number of casualties and economic damages are increasing. Accordingly, efforts to collect and process data have been made to predict and respond to disasters. However, because the data collected from a certain disaster is enormous and diverse, it is difficult to identify an exact disaster type and its situations at the early stage of a disaster. To that end, in this paper, we first classify disasters into six categories according to their characteristics and extend our ontology-based temporal knowledge graphs to contain these characteristics. Finally, to detect a disaster from temporal knowledge graphs, Graph Neural Networks (GNN) or other deep learning techniques can be useful. For the evaluation, we selected four disasters belonging to six categories and constructed temporal knowledge graphs for each disaster. Then, to see how quickly a disaster can be detected from the constructed graphs, we tested three GNN models, including Graph Convolutional Network (GCN), SageConv, and Graph Attention Network (GAT). Our experimental results show that temporal disaster knowledge graphs can accurately represent the characteristics of various disasters, enabling the detection of disasters from heterogeneous data collected at disaster sites.
AB - As unexpected disasters increase, the number of casualties and economic damages are increasing. Accordingly, efforts to collect and process data have been made to predict and respond to disasters. However, because the data collected from a certain disaster is enormous and diverse, it is difficult to identify an exact disaster type and its situations at the early stage of a disaster. To that end, in this paper, we first classify disasters into six categories according to their characteristics and extend our ontology-based temporal knowledge graphs to contain these characteristics. Finally, to detect a disaster from temporal knowledge graphs, Graph Neural Networks (GNN) or other deep learning techniques can be useful. For the evaluation, we selected four disasters belonging to six categories and constructed temporal knowledge graphs for each disaster. Then, to see how quickly a disaster can be detected from the constructed graphs, we tested three GNN models, including Graph Convolutional Network (GCN), SageConv, and Graph Attention Network (GAT). Our experimental results show that temporal disaster knowledge graphs can accurately represent the characteristics of various disasters, enabling the detection of disasters from heterogeneous data collected at disaster sites.
KW - disaster classification
KW - graph neural networks
KW - temporal knowledge graphs
UR - http://www.scopus.com/inward/record.url?scp=85189934360&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC60209.2024.10463410
DO - 10.1109/ICAIIC60209.2024.10463410
M3 - Conference contribution
AN - SCOPUS:85189934360
T3 - 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024
SP - 681
EP - 684
BT - 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024
Y2 - 19 February 2024 through 22 February 2024
ER -