Towards a Spatial Knowledge Mesh: A Metamodel for Federated and Interoperable Spatial Knowledge Graphs to Enable Geospatial Awareness, Integrity, Provenance, and Trust in Large Language Models

Abstract. Large language models (LLMs) have the potential to make geospatial decision support more accessible to non-technical users and under-resourced settings in public health, disaster response, and infrastructure planning. Compared with traditional geospatial workflows requiring specialized expertise, manual data integration, preprocessing, and static mapping, LLM-based GeoAI systems can enable users to explore spatial questions through natural language. However, LLMs lack an internal representation of geographic features, spatial networks, and domain-specific semantic relationships, and therefore require structured geospatial knowledge at runtime to reason over interconnected spatial systems. Building on prior work that introduced the Spatial Knowledge Mesh (SKM) as an architecture for managing dependencies and interoperability between interlinked spatial knowledge graphs (McEachen, N., Lewis, J., 2023), this paper makes three contributions. First, it defines a metamodel for publishing interoperable spatial knowledge graphs (iSKGs) as governed, versioned, and temporally valid knowledge assets. Second, it describes how iSKGs can be federated through shared geographic abstractions, geo-ontologies, provenance metadata, and change propagation mechanisms. Third, it shows how this publication model enables Geo-GraphRAG workflows in which LLM-based systems can retrieve and reason over spatial relationships with integrity, provenance, and trust (IPT). By enabling governments, spatial data infrastructures, and other institutions to publish networks of geographic features as interoperable knowledge assets, the SKM can help lower-resourced settings create locally relevant GeoAI applications and make public geospatial data more effective for decision support.