Ontology • Version 1.0
A Structured Model for Reality, Intelligence, Governance, and Action
This ontology is a living system. Concepts, relationships, frameworks, architectures, and systems may evolve as the DataUniversa ecosystem expands.
Foundation Primitives
Foundation Frameworks
Architectures
Systems
Core Ontology Objects
Introduction
Information systems were built to store data. AI systems were built to process data. Very few systems were built to explicitly represent reality itself.
DataUniversa was created from a different premise: Information becomes far more useful when it is connected to the things it describes, the evidence supporting it, the decisions it influences, the actions it enables, and the outcomes it produces.
Over many years, a collection of patents, provisional patents, software systems, and applied projects explored different aspects of this problem.
Initially these appeared to be separate inventions:
- Identity and provenance systems
- Physical asset intelligence systems
- Human movement intelligence systems
- Decision support systems
- AI governance systems
- Knowledge management systems
- Semantic valuation systems
The DataUniversa Model
DataUniversa organizes intelligence into four layers, moving from foundational concepts to operational systems.
Ontology Layers
Click any layer card to open its supporting data page.
Foundational Primitives
The smallest concepts required to represent reality, reasoning, governance, value creation, and action.
These primitives are the building blocks from which all higher-level structures are created.
Foundation Frameworks
Reusable structures built from multiple primitives.
Examples include:
Frameworks solve recurring intelligence problems.
Architectures
Integrated systems of frameworks designed to address broad domains.
Examples include:
Architectures define how frameworks work together.
Systems
Actual products, platforms, services, networks, and applications.
Examples include:
Systems are the operational implementations of the ontology.
Why This Ontology Exists
The purpose of the DataUniversa ontology is to create a common vocabulary for representing and reasoning about reality.
The ontology is designed for:
- Humans
- Organizations
- AI systems
- Autonomous agents
- Knowledge networks
- Decision systems
Its goal is not merely to store information, but to make information:
- Traceable
- Explainable
- Governable
- Actionable
- Valuable
- Interoperable
A Model for Connected Intelligence
Traditional databases store records. Knowledge graphs store relationships. AI models learn patterns.
DataUniversa is intended to provide a structure that connects all three.
By representing entities, observations, evidence, governance, value, decisions, and outcomes within a common framework, DataUniversa seeks to create an environment in which intelligence can be accumulated, shared, evaluated, governed, and improved over time.
The concepts that follow represent the current public ontology of the DataUniversa ecosystem.
They are intended as a foundation for future work in connected AI, decision intelligence, provenance-aware systems, semantic economies, human intelligence networks, and machine-operable representations of reality.