A formal, machine-readable model of meaning that makes data understandable across systems, organizations, and time.
An ontology is a shared vocabulary that defines what things are, how they relate to each other, and what rules govern them. It's not just a list of terms—it's a logical model that humans can read and machines can reason over.
In defense and intelligence operations, data comes from dozens of incompatible systems: sensors, databases, coalition partners, and legacy platforms. Without a shared ontology, each system speaks its own language, making integration expensive, error-prone, and slow.
Ontologies solve this by providing a common semantic layer—a single source of truth that every system can reference. This enables automated data fusion, explainable AI, and real-time decision support across the joint force.
Databases store facts. Ontologies define what facts mean. A database might record "Asset A is in Location B," but an ontology explains what "Asset," "Location," and "is in" actually represent—and what you can infer from that relationship.
Taxonomies organize things into hierarchical categories (e.g., "Aircraft > Fighter > F-35"). Ontologies do more: they capture complex relationships, constraints, and rules that enable logical reasoning and validation.
Controlled vocabularies are lists of standardized terms. Ontologies add formal definitions, relationships, and axioms that machines can process to answer queries, detect inconsistencies, and generate insights.
The categories that things belong to. Example: Person, Organization, Equipment.
The relationships between things. Example: worksFor, locatedIn, hasCapability.
Specific instances of classes. Example: John Smith, USAF, AIM-120 AMRAAM.
The rules and constraints. Example: "Every mission must have at least one assigned asset" or "No asset can be in two locations simultaneously."
Consider a logistics scenario where you need to track maintenance parts across bases, ships, and warehouses:
Equipment, Location, and Inventory Status. Queries work across the enterprise, and AI can reason about supply chain risks automatically. Ontos Cosmos uses widely adopted, non-proprietary standards to ensure your ontologies are portable, auditable, and interoperable:
W3C standard for representing information as subject-predicate-object triples. The foundation for linked data on the web.
W3C standard for encoding rich semantic relationships and logical constraints that enable automated reasoning.
W3C standard for validating RDF data against structural and business rules. Ensures data quality at ingest and during transformation.
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