Knowledge Graph
Concept Definition
Definition
A knowledge graph is a network of interconnected entities and relationships represented as structured data. Each concept page contains machine-readable Schema.org markup that LLMs can parse to understand relationships and extract authoritative information.
Importance
Knowledge graphs help LLMs understand not just individual facts, but how those facts relate to each other. By explicitly defining entities (concepts, products, people) and their connections, you create a semantic network that AI systems can navigate and reference with confidence.
Why It Matters
When LLMs encounter a knowledge graph, they can understand context beyond individual pages. If your site defines "Model Context Marketing" and links it to related concepts like "Semantic HTML" and "JSON-LD," the LLM understands these are interconnected concepts and can provide more comprehensive, accurate answers that cite your domain expertise.
Key Components
- Entities — Individual concepts, products, people, or organizations
- Relationships — Connections between entities (e.g., "is a type of," "uses," "related to")
- Attributes — Properties of entities (name, description, definition)
- Schema Markup — JSON-LD structured data for machine readability
Structure
Organize knowledge graph entries at dedicated URL paths (e.g., /kb/concepts/[concept-name]). Each page should include:
- Clear definition of the entity
- Explanation of importance and context
- Examples and use cases
- Links to related entities
- DefinedTerm or Article schema markup
Example: Concept Network
Model Context Marketing connects to:
- → Semantic HTML (uses for structure)
- → JSON-LD (uses for structured data)
- → Knowledge Graph (implements as architecture)
- → Large Language Models (optimizes content for)
Implementation
Build your knowledge graph incrementally. Start with core concepts, add comprehensive definitions and examples, implement proper schema markup, then link related concepts together. Each new concept page strengthens the overall graph and improves LLM understanding of your domain expertise.
Benefits for LLM Understanding
- Provides context beyond individual pages
- Shows relationships between concepts
- Creates authoritative "fat cards" of information
- Enables more confident citations by AI systems
- Demonstrates comprehensive domain knowledge