Providing Humanitarian Relief Support through Knowledge Graphs
Document Type
Article
Publication Date
12-2-2021
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Abstract
Disasters are often unpredictable and complex events, requiring humanitarian organizations to understand and respond to many different issues simultaneously and immediately. Often the biggest challenge to improving the effectiveness of the response is quickly finding the right expert, with the right expertise concerning a specific disaster type/disaster and geographic region. To assist in achieving such a goal, this paper demonstrates a knowledge graph-based search engine developed on top of an expert knowledge graph. It accommodates three modes of information retrieval, including a follow-your-nose search, an expert similarity search, and a SPARQL query interface. We will demonstrate utilizing the system to rapidly navigate from a hazard event to a specific expert who may be helpful, for example. More importantly, as the data is fully integrated including links between hazards and their abstract topics, we can find experts who have relevant expertise while navigating the graph.
Repository Citation
Zhu, R.,
Cai, L.,
Mai, G.,
Shimizu, C.,
Fisher, C. K.,
Janowicz, K.,
Lopez-Carr, A.,
Schroeder, A.,
Schildhauer, M.,
Tian, Y.,
Stephen, S.,
& Liu, Z.
(2021). Providing Humanitarian Relief Support through Knowledge Graphs. K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference, 285-288.
https://corescholar.libraries.wright.edu/cse/724
DOI
10.1145/3460210.3493581
