Executive Summary
The Practice-Area Depth Index (PADI) is a technical standard (v1.1/v2.0) designed to transition professional expertise from static, natural-language descriptions into machine-readable, deterministic data structures. By treating knowledge governance as code, PADI enables a "Sovereign Source of Truth" that anchors AI systems and autonomous agents in rigid, verifiable logic.
1. The Core Architecture
PADI utilizes Semantic Web standards to create a structured classification system for technical and professional domains.
Semantic Foundation
SKOS (Simple Knowledge Organization System): Used for defining the basic hierarchy and concept relationships.
OWL 2 (Web Ontology Language): Establishes the formal logic and complex relationships between practice areas.
RDF/Turtle (.ttl): The primary serialization format for ensuring portability and readability across knowledge graphs.
The PADI Scale
The taxonomy utilizes a standardized indexing scale to quantify expertise:
L1 - Awareness: Conceptual understanding.
L2 - Foundational: Practical application under supervision.
L3 - Intermediate: Autonomous execution of standard procedures.
L4 - Advanced: System architecture and strategic leadership.
L5 - Expert: Global innovation and standard-setting.
2. Knowledge Governance-as-Code (KGac)
The defining feature of the PADI framework is its shift from passive documentation to active, enforceable code.
Declarative Validation (SHACL)
PADI uses SHACL (Shapes Constraint Language) to act as a "linter" for professional data. This ensures that every entry in a knowledge graph adheres to the defined schema. If a node lacks a required depth index or metadata tag, the system fails the validation, preventing data "drift" or corruption.
Deterministic Grounding
Unlike probabilistic AI models that guess competency based on context, PADI provides a deterministic anchor. This allows autonomous agents to:
Parse verified RDF data.
Validate against SHACL shapes.
Execute decisions (e.g., job matching or task delegation) with 1:1 precision.
3. Technical Implementation & Settlement
The PADI framework is operationalized through Settlement Nodes—functional environments where governance logic is applied to real-world datasets.
Version Control: Managed via Git, allowing for branching, merging, and auditing of the taxonomy.
Persistent Identification: Registered via Zenodo (DOI: 10.5281/zenodo.18894084) to ensure citable, immutable versions of the standard.
Operational Dashboarding: Deployment via Streamlit or similar frameworks to visualize knowledge graph health and validation status.
4. Conclusion
Knowledge Governance-as-Code via the PADI Taxonomy represents the "Librarian's Mandate" in the age of AI: the intentional, architectural control of information to ensure that digital systems remain accurate, transparent, and sovereign.
Standard Metadata
