A scalable ontology-driven architecture for situational awareness and decision support in multi-domain operations

Abstract

Creating coherent and explainable situational awareness (SA) in multiple operational domains remains a persistent challenge for modern command-and-control (C2) systems. Heterogeneous data sources, inconsistent semantics, and fragmented reasoning mechanisms hinder timely and accountable decision-making in multi-domain operations (MDOs). This paper presents the design of a scalable, ontology-driven architecture that unifies data ingestion, semantic integration, and predictive reasoning within a modular framework for SA and decision support. The architecture employs an ontology-centric semantic core to ensure interoperability and traceability across domains while maintaining a clear separation between the data, reasoning, and analytics layers. Each component is motivated by operational and cognitive requirements, with an emphasis on scalability, explainability, and integration readiness. A conceptual integration and validation path is outlined to guide future evaluation within synthetic and simulation-based environments such as High Level Architecture (HLA) federations and C2 Systems—Simulation Systems Interoperation Standard (C2SIM) ecosystems. Rather than reporting empirical results or system-level performance metrics, the paper offers a theoretically grounded, design-oriented contribution, explicitly positioned as a methodological and architectural framework at an early stage of technological maturity.