Industry Examples
Sector-specific graph models showing how real organisations structure their data. Each includes an interactive diagram, relationship taxonomy, and design rationale.
Data Modelling
Effective knowledge graph design starts with understanding how your domain’s entities, relationships, and constraints map to a property graph. This section provides worked examples — real-world models you can explore interactively and adapt to your own data.
Why Property Graphs?
Property graphs model the world as nodes (entities), relationships (how they connect), and properties (attributes on both). Unlike relational tables where connections are implicit in foreign keys, a property graph makes relationships first-class citizens — queryable, typed, and carrying their own data.
This matters when:
- The questions you need to answer are about paths and connections, not just rows
- Your data spans multiple systems with overlapping entities that need resolution
- Regulatory or audit requirements demand lineage and provenance across processes
- You need to traverse multi-hop relationships without expensive joins
What’s Here
Use-Case Templates
Cross-industry patterns that recur across sectors. Start from a proven template and adapt it to your domain.
Data Lineage → Organisation Data Lineage → Hierarchical Lexical Graph → Customer 360 →
Modelling Conventions
All examples in this section follow consistent property graph conventions:
| Element | Convention | Example |
|---|---|---|
| Node labels | PascalCase | Legislation, ProcessStep, Customer |
| Relationship types | UPPER_SNAKE_CASE | MANDATES, FEEDS_INTO, OWNS |
| Property keys | camelCase | year, formId, riskScore |
| Node types | Colour-coded by category | Navy = legislation, teal = policy, amber = forms |