Concept
Knowledge Graph
A connected representation of entities, relationships, context, and evidence for reasoning and discovery.
Position in the Trusted Data Framework
Knowledge Graph
Living Graph View
Knowledge Graph in the concept network
Drag nodes to adjust the view, double-click a node to expand its neighbourhood, and right-click a node to hide it while preserving the exploration context.
Relationship Matrix
Knowledge Graph as a network node
Ontology defines the graph schema, including entity classes, relationship types, and valid patterns of meaning.
The semantic layer supplies governed definitions and mappings that make graph data consistent across sources.
Reality Mapping anchors graph nodes to real-world objects, events, and responsibilities.
A knowledge graph gives AI systems structured context, traceable relationships, and paths for explanation.
Knowledge graphs connect evidence, dependencies, and consequences so decisions can be evaluated in context.
Concept Relationships
How Knowledge Graph works with other concepts
Ontology defines the graph schema, including entity classes, relationship types, and valid patterns of meaning.
An infrastructure graph uses the ontology to distinguish Asset, Component, Location, Sensor, Event, and Control.
What schema is needed before graph connections can be trusted?
The semantic layer supplies governed definitions and mappings that make graph data consistent across sources.
Multiple source-system customer identifiers are mapped into one governed Customer concept before entering the graph.
Which semantic mappings are needed to prevent duplicate or conflicting graph nodes?
Reality Mapping anchors graph nodes to real-world objects, events, and responsibilities.
A physical asset, its digital twin, maintenance record, inspection event, and risk decision become connected evidence.
Which graph nodes represent real things, and what evidence proves that mapping?
A knowledge graph gives AI systems structured context, traceable relationships, and paths for explanation.
A risk assistant can explain which supplier, component, location, and control evidence influenced a recommendation.
What connected context must AI retrieve before making or explaining a decision?
Knowledge graphs connect evidence, dependencies, and consequences so decisions can be evaluated in context.
A capital planning decision can trace affected assets, service risks, dependencies, and previous interventions.
Which decisions require relationship-based evidence rather than flat reports?
Definition
A knowledge graph represents domain knowledge as connected entities and relationships. It combines data, meaning, context, and evidence in a structure that people and machines can navigate.
Why It Matters
Complex organisations rarely need one more isolated dataset. They need connected knowledge that can answer relationship-based questions and support explainable AI.
Role in the Trusted Data Framework
Knowledge graphs sit in the Knowledge layer. They turn semantic models and trusted data into a navigable structure for reasoning, discovery, and AI augmentation.
Practical Examples
- Connecting assets, locations, events, documents, risks, and decisions.
- Supporting retrieval-augmented generation with governed context.
- Mapping dependencies across infrastructure, suppliers, systems, and controls.
Consulting Questions
- Which entity relationships matter most for decisions?
- What evidence should be attached to claims and recommendations?
- Which graph questions cannot be answered by current data platforms?
- How will the graph remain aligned with source systems?
- Which users need graph navigation, analytics, or AI access?
See how this concept works in a shared preparation model: follow it through the Data Kitchen.