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

TL;DR

This paper presents GeoAI techniques to automatically identify causal relationships among cascading disaster events, enhancing knowledge graphs for environmental resilience research.

Contribution

It introduces spatially and temporally-enabled semantic rules for modeling causality in disaster events within knowledge graphs, addressing limitations of isolated event modeling.

Findings

Enables automatic extraction of causal relationships between events.

Enriches knowledge graphs with linked cascading events.

Supports improved environmental resilience analysis.

Abstract

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.