GeoAI for Knowledge Graph Construction: Identifying Causality Between Cascading Events to Support Environmental Resilience Research
This work addresses a gap in environmental resilience research by enabling better modeling of cascading disaster events in knowledge graphs, though it is incremental as it builds on existing GeoAI and semantic rule methods.
The paper tackles the problem of missing linkages among cascading events in knowledge graphs by introducing GeoAI solutions to identify causality between disaster events using spatially and temporally-enabled semantic rules, demonstrating automatic extraction of causal relationships to enrich event knowledge bases.
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.