Experiment
Living Evidence Graph
Maintain a continuously improving representation of the people, places, organisations, interventions, evidence and outcomes Defra knows about.
01
Problem to address
Defra’s real-world objects are highly connected, but systems tend to store transactions separately. People need to understand relationships, conflicts and change, not just retrieve records. At present those relationships are often reconstructed by people.
02
What the evidence tells us
Regulators need one picture of a producer. Field officers need to see how farmer, holding, parcel and agreement relate. Case workers rarely see the whole picture. With millions of data points, people also need help spotting which claims deserve attention first.
03
The proposition to explore
The idea
Represent important people, places, organisations, interventions and evidence as a living network of relationships with provenance, time and confidence.
04
Hypothesis
If Defra represents important entities and evidence as an explicit, provenance-rich graph, the same connected knowledge can support operational decisions, anomaly detection, user-facing views and policy learning without every team rebuilding joins.
05
How it could work
Model entities and relationships explicitly. Each relationship carries source, time, confidence and status. New verified evidence updates the graph; conflicting evidence is shown rather than silently overwritten. AI can surface candidate connections or unusual changes, but the graph remains governed.
06
Example: bring it to life
Before visiting a farm, a field officer doesn't search five systems. They ask:
“What do we currently believe about this holding?”
The AI assembles:
14 parcels are associated with this holding.
3 active agreements apply.
Parcel 42 has conflicting boundary information.
Satellite imagery suggests a possible land-use change since the last inspection.
Natural England recorded an intervention nearby.
Two sources disagree about the hedge classification.
Confidence: moderate.
Crucially, AI doesn't pretend there is one truth when the evidence conflicts. It exposes what Defra believes, why it believes it, and how confident it is.
07
AI opportunity
AI can assist entity resolution, infer candidate relationships, detect conflicts, identify emerging signals and generate evidence-backed summaries. Every inferred relationship must retain provenance and confidence, with human/governed confirmation where it matters.
08
Potential value
- A reusable intelligence layer across services
- Faster detection of conflicting or changing evidence
- Better operational and policy context
- Foundation for Digital Twin and regulatory-network concepts
09
Reduce uncertainty without overcommitting
Smallest meaningful experiment
Create a graph for one narrow journey with 3–5 entity types and real source metadata. Compare task completion against the current search-and-join workflow.
10
Open questions and risks
What we still need to learn
- Inference must never be mistaken for verified truth
- Graph scope can grow uncontrollably
- Needs clear stewardship, vocabulary and access control