Knowledge graphs tell you WHAT entities exist and HOW they relate. Context graphs tell you WHEN, WHERE, WHY, and FOR WHOM those relationships matter.
Knowledge Graphs vs. Context Graphs
A knowledge graph stores: "iPhone 15 Pro → is_a → Smartphone → made_by → Apple"
A context graph adds: - Temporal context: Search demand for this entity peaks in September (launch month) - Behavioral context: Users searching this entity from mobile have 3x higher purchase intent - Situational context: Users in Atlanta searching this entity are likely comparing AT&T vs. Verizon plans - Competitive context: Competitor X ranks for this entity with comparison content, not product pages
Architecture of a Context Graph
Building on top of knowledge graph infrastructure:
### Layer 1: Entity Graph (Knowledge) Static relationships between entities — taxonomy, attributes, synonyms.
### Layer 2: Temporal Graph Time-series data overlaid on entities — seasonality patterns, trend velocity, decay rates.
### Layer 3: Behavioral Graph User interaction patterns — click-through rates by entity, scroll depth, conversion paths.
### Layer 4: Competitive Graph Competitor entity coverage — who ranks for what, content format distribution, gap opportunities.
### Layer 5: Intent Graph Dynamic intent mapping — how user intent for an entity shifts based on time, location, and device.
Implementation for SEO
Context graphs enable: - Dynamic content prioritization — surface content that matches current temporal and behavioral context - Personalized internal linking — recommend links based on user journey context, not just topical relevance - Predictive content planning — create content BEFORE demand peaks based on temporal patterns - Competitive response automation — trigger content creation when competitors gain entity coverage
The Technical Stack
- —Knowledge graph: Neo4j with custom ontology
- —Temporal layer: BigQuery time-series with Python forecasting models
- —Behavioral layer: GA4 + BigQuery event streams
- —Competitive layer: Automated crawl + SERP monitoring
- —Intent classification: Fine-tuned BERT models for multi-label intent prediction