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[DSB:THEORY.SEMANTIC_BINDING] [DSI:NAME=SEMANTIC_BINDING_OVERVIEW_AND_CONTENTS;ROLE=REFERENCE;AUTHOR=SIMON_MACFARLANE;VERSION=1_0;DATE=DEC2025] [DSM:SYSTEM=SEMANTIC_BINDING;AUDIENCE=PUBLIC,PROFESSIONAL,AUTHORING_SYSTEMS]


[SSB:THEORY.SEMANTIC_BINDING.CONTENTS.TABLE.REFERENCE.6] [SSI:TITLE=TABLE_OF_CONTENTS;AUTHORITY=SECONDARY;REF=3] [SSM:SECTION=REFERENCE;INTENT=APPLICATION;ABSTRACTION=LOW]

Semantic Binding

Table of Contents

# Core Specification

SB-001 — Introduction & Purpose
Defines what Semantic Binding is, why it exists, and where it sits in the system.

SB-002 — Semantic Binding Taxonomy
Defines the canonical taxonomy: DOMAIN, OBJECT, CATEGORY, UNIT.

SB-003 — The Six Semantic Anchors
Defines the semantic contract anchors (DSB, DSI, DSM, SSB, SSI, SSM).

SB-004 — Abstraction in Semantic Binding
Defines abstraction as a first-class control signal (HIGH / MEDIUM / LOW).

SB-005 — Capitalisation, Immutability, and Naming Rules
Defines the enforcement rules that make Semantic Binding stable and machine-safe.

SB-006 — Authoring and Implementation Patterns
Defines normative rules for writing semantically bound content.

SB-007 — Retrieval, Prompting, and Agent Behaviour
Defines how Semantic Binding governs retrieval, prompting, and AI behaviour.


# Operational Guide

SB-008 — Implementing Semantic Binding (Practical Guide)
Provides step-by-step guidance, mental models, and applied execution patterns.


[SSB:THEORY.SEMANTIC_BINDING.SUMMARY.USAGE.GUIDANCE.7] [SSI:TITLE=HOW_TO_USE_THIS_KNOWLEDGE_BASE;AUTHORITY=SECONDARY;REF=4] [SSM:SECTION=REFERENCE;INTENT=APPLICATION;ABSTRACTION=LOW]

How to Use This Knowledge Base

For executives / architects

  1. Page 0 (this document)
  2. SB-001
  3. SB-003
  4. SB-007

For authors

  1. SB-001 → SB-004
  2. SB-006
  3. SB-008

For system implementers

  1. SB-002 → SB-005
  2. SB-007
  3. SB-008

[SSB:THEORY.SEMANTIC_BINDING.STATUS.OVERVIEW.ACTIVE.8] [SSI:TITLE=STATUS;AUTHORITY=SECONDARY;REF=5] [SSM:SECTION=STATUS;INTENT=STATUS;ABSTRACTION=LOW] s

Status

Semantic Binding is a complete, enforceable system for building trustworthy, scalable, AI-ready knowledge.