canonical-ks 1.0.1

Canonical Knowledge Structure — Reference Implementation

Canonical Knowledge Structure (CKS)

A universal, representation-independent foundation for knowledge.

CKS is an open specification that defines how knowledge can be represented, validated, exchanged, and evolved independently of programming languages, document formats, databases, or AI systems.

Rather than introducing yet another serialization format or programming language, CKS defines a canonical semantic layer shared by humans, software, and artificial intelligence.

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Why CKS?

Today the same knowledge exists simultaneously in many incompatible forms:

• documents
• databases
• JSON
• XML
• source code
• knowledge graphs
• ontologies
• AI prompts
• APIs

Each representation describes the same underlying knowledge differently.

CKS separates knowledge itself from every concrete representation.

Knowledge
      │
      ▼
Canonical Knowledge Structure (CKS)
      │
 ┌────┼───────────────┐
 ▼    ▼               ▼
JSON Python Database Natural Language

Representations may change.

Canonical knowledge remains the same.

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Core Principles

CKS is founded on four simple principles.

Knowledge exists independently of its representation.

Knowledge is not JSON.

Knowledge is not a PDF.

Knowledge is not source code.

Representations are temporary.

Knowledge is not.

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Structure preserves meaning.

Meaning is preserved by canonical structure rather than by syntax.

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Representation preserves structure.

Different representations may express the same canonical structure.

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Canonical operations belong to knowledge itself.

Validation.

Serialization.

Comparison.

Evolution.

Inspection.

These are operations on knowledge—not on files, databases, or programming languages.

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Architecture

The CKS ecosystem consists of implementation-independent specifications.

Specification	Purpose
CKS-000	Foundations and terminology
CKS-001	Canonical semantic model
CKS-002	Knowledge construction
CKS-003	Canonical serialization
CKS-004	Structure evolution
CKS-005	Validation
CKS-006	Reference Engine
CKS-007	Canonical Knowledge Interface
CKS-008	Conformance
CKS-009	Reference Knowledge Corpus
CKS-B001	Python Reference Implementation

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Features

The current Python reference implementation provides:

• Immutable Canonical Knowledge Objects
• Canonical Relations
• Immutable Knowledge Structures
• Canonical JSON Serialization
• Deterministic Validation Pipeline
• Diagnostic System
• Reference Engine
• Canonical Public API
• Structural Comparison
• Projection
• Extraction
• Inspection
• Conformance Test Suite
• Command-Line Interface (validate, parse, inspect, evolve, schema, plugin)
• Structural Evolution (Genesis/Decay operators)
• Configurable Severity Thresholds
• HTML and Markdown Report Formatters
• Batch Validation (multiple files)
• JSON‑LD, Turtle, RDF/XML Import (via cks convert)
• JSON‑LD, Turtle, RDF/XML Export (via cks export)

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Design Goals

CKS is designed to be:

• deterministic
• immutable
• observationally pure
• representation-independent
• implementation-independent
• language-independent
• suitable for formal verification

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Current Repository

This repository contains the official Python Reference Implementation of the Canonical Knowledge Structure specifications.

Currently implemented:

• ✅ Canonical Knowledge Objects
• ✅ Canonical Relations
• ✅ Canonical Knowledge Structures
• ✅ Canonical Serialization
• ✅ Validation Pipeline
• ✅ Diagnostic System
• ✅ Reference Engine
• ✅ Canonical Public Interface
• ✅ Command-Line Interface
• ✅ Structural Evolution (CKS‑004)
• ✅ Reference Knowledge Corpus
• ✅ Conformance Test Suite (114 tests)
• ✅ PyPI Publication
• ✅ Import/Export Adapters (JSON‑LD, Turtle, RDF/XML)

Planned:

• Constraint Libraries (additional built‑in constraints)
• Additional language implementations (Rust, TypeScript)

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Installation

From PyPI:

pip install canonical-ks

Or from source:

git clone https://github.com/Deus-corp/CKS.git
cd CKS
pip install -e .

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Quick Example

from cks import (
    construct,
    validate,
    serialize,
)

from cks.core import (
    KnowledgeObject,
    ObjectIdentity,
)

obj = KnowledgeObject(
    identity=ObjectIdentity(
        id="obj-1",
        type="Definition",
        name="Knowledge",
    )
)

structure = construct([obj])

result = validate(structure)

print(result.is_valid)

print(serialize(structure))

Or use the command line:

# Validate a knowledge structure
cks validate examples/corpus/valid_theory_example.json

# Evolve a structure by adding an object
cks evolve examples/corpus/valid_theory_example.json examples/corpus/evolve_add.json

Or convert between formats:

# Convert JSON‑LD to CKS
cks convert examples/corpus/person.jsonld --format json-ld --output person.cks.json

# Export CKS to Turtle
cks export examples/corpus/valid_theory_example.json --format turtle --output theory.ttl

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Testing

Run the complete conformance suite:

python -m pytest -v

Current status:

• 114 tests
• all passing

The test suite verifies:

• deterministic behaviour
• immutability
• observational purity
• canonical serialization
• validation correctness
• public API conformance
• structural equivalence

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Documentation

The complete specification is published separately.

Core specifications:

• CKS-000 — Foundations
• CKS-001 — Core Specification
• CKS-002 — Construction
• CKS-003 — Serialization
• CKS-004 — Evolution
• CKS-005 — Validator
• CKS-006 — Reference Engine
• CKS-007 — Canonical Knowledge Interface
• CKS-008 — Conformance

DOI:

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Project Status

Current implementation status:

Component	Status
Core Model	✅ Complete
Serialization	✅ Complete
Validation	✅ Complete
Reference Engine	✅ Complete
Public API	✅ Complete
Test Suite	✅ Passing
CLI	✅ Complete
Structural Evolution	✅ Complete
Advanced Validation	✅ Complete
Import/Export Adapters	✅ Complete

The current implementation serves as the reference implementation of the existing CKS specifications.

Future work focuses primarily on expanding the specification rather than redesigning the implemented components.

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Vision

CKS aims to establish a universal semantic foundation for knowledge exchange between:

• humans
• software
• databases
• distributed systems
• artificial intelligence

through a single canonical representation of knowledge that is independent of every concrete implementation.

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License

MIT