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