DisasterLex: An Expert Concept-to-Schema Knowledge Graph for Geospatial Reasoning in Disaster Analytics
arXiv:2605.30538v1 Announce Type: new Abstract: Disasters are inevitable and increasingly costly, and effective response depends on querying structured tabular data: precise, information-dense records of hazard, exposure, vulnerability, and lifeline infrastructure that underpin disaster management. Current text-to-SQL methods enable natural-language access to such tables but transfer poorly to the disaster domain, where queries span heterogeneous geospatial schemas and require reasoning over causal relations. We introduce DisasterLex, a knowledge-graph-mediated framework that inserts an Expert
The increasing frequency and cost of disasters, coupled with the limitations of current text-to-SQL approaches for complex geospatial data, are driving demand for more sophisticated disaster analytics solutions.
DisasterLex represents a significant advancement in leveraging AI for disaster management, enabling more precise and rapid information extraction from heterogeneous data, which can improve response effectiveness and resource allocation.
The ability to query structured tabular data in the disaster domain with natural language, while incorporating causal reasoning and geospatial schema, moves beyond traditional text-to-SQL limitations toward more intelligent decision support.
- · Disaster Management Agencies
- · Geospatial AI Developers
- · Insurance Industry
- · Emergency Services
- · Traditional Data Analytics Providers (without AI integration)
- · Manual Disaster Data Processors
Improved speed and accuracy in disaster damage assessment and resource deployment.
Reduced economic losses and human casualties due to more effective disaster preparedness and response.
The establishment of new standards for AI-driven, knowledge graph-based disaster analytics, influencing urban planning and climate adaptation strategies.
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Read at arXiv cs.LG