Abstract
<jats:p>This technical note presents a reference architecture for constructing multisource knowledge graphs in air-gapped, domain-specific AI systems. The architecture addresses recurring problems in military, intelligence, and secure-enterprise environments: integrating heterogeneous authoritative data sources while preserving source schema fidelity, enabling deterministic semantic resolution, and operating without external network dependencies. While this is a geospatial implementation, the architectural principles in the core design are transferable. The implementation is organized around four separable layers—schema registry, domain ontology, reasoning patterns, and relationship vocabulary—and adopts a canonical-with-aliasing integration strategy that supports cross-schema reasoning without forcing premature schema con-vergence. These patterns are validated through a geospatial intelligence implementation supporting US Army operations and demonstrate how abstract design principles translate into an operationally relevant system. This knowledge graph is designed to serve as the semantic substrate for router-based AI systems. A companion technical note (Drouillard and Lewis 2026) describes the geospatial AI (GeoAI) agent stack, which is a router-based orchestration architecture that coordinates multiple retrieval backends and reasoning tools. Within that architecture, the knowledge graph functions as a specialized retrieval backend that runs alongside document retrieval and vector search, serving queries that require structured entity-relationship reasoning, provenance tracking, or deterministic semantic resolution. The router directs spatial relationship queries (e.g., “which roads cross this river”), multihop dependency queries (e.g., “what infrastructure depends on this power station”), and schema-resolution queries (e.g., “find all transportation features in Multinational Geospatial Co-production Program [MGCP] format”) to the knowledge graph while routing conceptual or analytical questions to document retrieval. This technical note focuses exclusively on the knowledge graph architecture; the broader orchestration patterns and routing logic are detailed in the companion paper. While the examples presented are geospatial, the architectural principles, validation strategies, and design tradeoffs documented here have broader applicability where deterministic semantic integration is required under air-gapped constraints. The geospatial instantiation serves as a concrete demonstration of abstract patterns that may inform future knowledge graph efforts in other US Army Engineer Research and Development Center (ERDC) research domains.</jats:p>