# Ontology Guide: The Semantic Bridge for Agentic AI

## The Problem: AI Needs Context, Not Just Rules
Large Language Models (LLMs) and autonomous AI agents struggle with raw compliance documentation or scattered JSON constraint files. While our JSON Schemas are excellent for deterministic execution (the "Physical Plane"), they lack semantic depth. If an AI agent is instructed to "maximize efficiency," it doesn't innately understand that a specific latency metric depends on a specific rule that is legally bound to an EU AI Act requirement.

## The Solution: A Semantic Digital Twin
The ODGS OWL Ontology (`ontology_graph.owl`) provides a formalized, machine-readable graph of the entire **Constitutional Stack**. It serves as a Semantic Digital Twin of an organization's governance posture, explicitly defining how `Metrics` relate to `Rules`, `Rules` to `Dimensions`, and `Dimensions` to authoritative `SovereignDefinitions` (like the EU AI Act or internal SLAs).

## Why Use the ODGS Ontology?

1. **Formal Understanding of Boundaries:**
   Provide your LLM Agents with a structural understanding of regulatory boundaries *before* they take action. When an agent queries the ontology, it learns the exact lineage of constraints, reducing the risk of unsafe autonomous decisions ("AI Alignment").

2. **Semantic Interoperability:**
   Built on the W3C Web Ontology Language (OWL) standard, the ODGS ontology natively integrates with existing enterprise knowledge graphs and graph databases (Neo4j, AWS Neptune).

3. **Reasoning & Inference:**
   Axiomatic definitions allow semantic reasoners (like HermiT or Pellet) to automatically infer new governance relationships or flag logical contradictions in a company's ruleset before deployment.

## Key Ontology Classes
The graph connects the declarative intent of governance to the mechanical reality of execution:
- **`SovereignDefinition`**: The authoritative source (e.g., an article of law, MSA, or internal policy).
- **`Dimension`**: The conceptual area of governance (e.g., Transparency, Data Quality, Privacy).
- **`Rule`**: The mechanical constraint evaluating the data.
- **`Metric`**: The specific data point being evaluated.
- **`Process`**: The business flow generating the data.

*By adopting the ODGS Ontology, engineering teams can elevate their autonomous agents from executing blind rules to understanding the fundamental governance context of their environment.*
