Comparison
Context Graph vs Knowledge Graph
Both context graphs and knowledge graphs are powerful tools for organizing information, but they serve fundamentally different purposes. Understanding when to use each is critical for building effective AI systems.
Quick Answer
Knowledge graphs store static facts and relationships between entities. Context graphs capture dynamic decision traces, including the reasoning, exceptions, and temporal context behind each decision. Knowledge graphs tell you "what exists"; context graphs tell you "why it was decided."
What is a Knowledge Graph?
A knowledge graph is a structured representation of facts about entities and their relationships. Originally popularized by Google in 2012, knowledge graphs organize information as nodes (entities) and edges (relationships).
For example, a knowledge graph might store: "Apple (Company) → founded_by → Steve Jobs" or "Paris → capital_of → France". These are static facts that describe the world.
Knowledge Graph Characteristics
- ●Stores entities (people, places, things, concepts)
- ●Captures relationships between entities
- ●Represents current state of knowledge
- ●Optimized for semantic search and information retrieval
- ●Uses standards like RDF, OWL, and SPARQL
What is a Context Graph?
A context graph is a living record of decision traces stitched across entities and time. Rather than storing static facts, context graphs capture the reasoning behind decisions—the exceptions, overrides, precedents, and cross-system context.
For example, a context graph might store: "Discount_Request_#1234 → approved_because → Customer_Tier_1 AND Q4_Revenue_Target AND VP_Override". This captures not just what happened, but why it was allowed to happen.
Context Graph Characteristics
- ●Stores decision traces with full reasoning
- ●Captures temporal context—when and why decisions changed
- ●Includes provenance metadata—who decided, what evidence
- ●Optimized for AI agent decision-making
- ●Enables precedent search for similar past decisions
Side-by-Side Comparison
| Aspect | Knowledge Graph | Context Graph |
|---|---|---|
| Primary Purpose | Store facts and relationships | Capture decision reasoning |
| Data Type | Static entities | Dynamic decision traces |
| Temporal Aspect | Current state snapshot | Historical evolution |
| Key Question | "What is X?" | "Why was X decided?" |
| Optimized For | Information retrieval | AI agent autonomy |
| Example Query | "Who founded Apple?" | "Why was this discount approved?" |
| Standards | RDF, OWL, SPARQL | Emerging (MCP, custom) |
| Audit Trail | Limited | Built-in with provenance |
When to Use Each Approach
Use a Knowledge Graph When:
- ✓Building semantic search applications
- ✓Creating recommendation systems
- ✓Organizing product catalogs
- ✓Powering Q&A systems with factual data
- ✓Integrating data from multiple sources
Use a Context Graph When:
- ✓Enabling AI agent autonomy
- ✓Capturing decision precedents
- ✓Building audit trails for compliance
- ✓Automating exception handling
- ✓Preserving tribal knowledge
Can They Work Together?
Yes! In fact, the most powerful enterprise AI systems use both knowledge graphs and context graphs together:
- ●Knowledge graphs provide the foundational understanding of entities and their relationships
- ●Context graphs add the decision layer, capturing how those entities interact in real business processes
Think of it this way: a knowledge graph tells an AI agent what a "Tier 1 Customer" is. A context graph tells the agent how the organization has historically handled Tier 1 customer requests differently.
Frequently Asked Questions
What is the main difference between a context graph and a knowledge graph?
Knowledge graphs store static facts and entity relationships, while context graphs capture dynamic decision traces including reasoning, exceptions, and temporal context. Context graphs answer "why was this decided?" while knowledge graphs answer "what is this?"
When should I use a context graph instead of a knowledge graph?
Use a context graph when you need to capture decision reasoning for AI agents, maintain audit trails, or enable precedent-based automation. Use a knowledge graph for static entity relationships, semantic search, and information retrieval.
Is a context graph a type of knowledge graph?
Context graphs expand upon knowledge graph structures by incorporating additional information such as time validity, source provenance, and decision reasoning. You can think of context graphs as knowledge graphs optimized specifically for AI decision-making.