Canonical Knowledge Structure (CKS)
A universal, representation-independent semantic foundation for knowledge.
The Canonical Knowledge Structure (CKS) project defines a formal semantic model for representing, validating, exchanging, and evolving knowledge independently of programming languages, document formats, databases, or artificial intelligence systems.
CKS separates knowledge itself from the representations used to store or communicate it.
Why CKS?
The same knowledge is often duplicated across many incompatible representations.
- Documents
- Databases
- JSON
- XML
- Source code
- Knowledge graphs
- APIs
- AI prompts
Each representation describes the same concepts differently.
CKS introduces a canonical semantic layer shared by all representations.
Knowledge
│
▼
Canonical Knowledge Structure
│
┌────┼───────────────┐
▼ ▼ ▼
JSON Python Database Natural Language
Representations may evolve over time.
The canonical knowledge remains unchanged.
Design Principles
CKS is founded on four fundamental principles.
Representation Independence
Knowledge exists independently of the syntax used to describe it.
JSON, Python objects, databases, and documents are merely different representations of the same underlying semantic structure.
Canonical Semantics
Meaning is preserved through canonical structure rather than implementation-specific syntax.
Deterministic Behaviour
Every canonical operation produces the same observable result for identical inputs.
Observational Purity
Canonical operations never modify their inputs.
Construction, validation, serialization, comparison, and inspection are observationally pure.
Project Architecture
The CKS ecosystem is organised as a family of independent specifications.
CKS-000
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CKS-001
│
├─────────────┐
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CKS-002 CKS-003
│ │
└──────┬──────┘
▼
CKS-004
│
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CKS-005
│
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CKS-006
│
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CKS-007
│
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CKS-008
│
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CKS-B001-PY
Each specification defines one canonical aspect of the ecosystem.
Reference Implementation
This repository contains the official Python reference implementation.
Current components include:
- immutable semantic model;
- canonical serializer;
- validator with constraint registry;
- reference engine;
- structural evolution (Genesis/Decay operators);
- command-line interface (CLI);
- public interface;
- conformance tests (116 tests);
- reference corpus (valid and invalid examples);
- documentation.
Documentation
The documentation is organised into several sections.
| Section | Description |
|---|---|
| Getting Started | Installation and first steps |
| Concepts | Fundamental ideas and terminology |
| Architecture | Internal organisation of the project |
| API Reference | Public Python interface |
| Examples | Practical usage examples |
| Specifications | Complete formal CKS specifications |
Learning Path
New users are encouraged to explore the project in the following order.
Getting Started
│
▼
Concepts
│
▼
Architecture
│
▼
API Reference
│
▼
Examples (including CLI and Corpus)
│
▼
Specifications
This progression introduces the conceptual foundations before the formal specification.
Current Status
The CKS specifications are stable and continue to evolve through versioned releases.
The Python implementation serves as the first canonical implementation of the CKS ecosystem.
Completed milestones include:
- ✅ Core semantic model (CKS‑001)
- ✅ Canonical serialization (CKS‑003)
- ✅ Validation pipeline (CKS‑005)
- ✅ Reference Engine (CKS‑006)
- ✅ Canonical Knowledge Interface (CKS‑007)
- ✅ Structural Evolution (CKS‑004)
- ✅ Command-Line Interface
- ✅ Reference Corpus
Future work includes:
- additional reference implementations (Rust, TypeScript);
- extended constraint libraries;
- interoperability tooling;
- domain-specific knowledge models;
- SDK and plugin architecture.
Open Source
CKS is developed as an open specification and open-source reference implementation.
Contributions are welcome.
See the following documents for additional information:
- CONTRIBUTING.md
- CODE_OF_CONDUCT.md
- SECURITY.md
- CHANGELOG.md
- ROADMAP.md
License
The CKS reference implementation is released under the MIT License.