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Knowledge graph technology is considered a powerful and semantically enabledsolution to link entities, allowing users to derive new knowledge by reasoningdata according to various types of reasoning rules. However, in building such aknowledge graph, events modeling, such as that of disasters, is often limitedto single, isolated events. The linkages among cascading events are oftenmissing in existing knowledge graphs. This paper introduces our GeoAI(Geospatial Artificial Intelligence) solutions to identify causality amongevents, in particular, disaster events, based on a set of spatially andtemporally-enabled semantic rules. Through a use case of causal disaster eventsmodeling, we demonstrated how these defined rules, including theme-basedidentification of correlated events, spatiotemporal co-occurrence constraint,and text mining of event metadata, enable the automatic extraction of causalrelationships between different events. Our solution enriches the eventknowledge base and allows for the exploration of linked cascading events inlarge knowledge graphs, therefore empowering knowledge query and discovery.

GeoAI for Knowledge Graph Construction: Identifying Causality Between Cascading Events to Support Environmental Resilience Research