Knowledge graphs — explained clearly
A knowledge graph is our specialty: the technology behind an AI that should answer from verified expert knowledge rather than guess. This hub explains, neutrally and soundly, what's behind it — and shows how we turn it into a digital product.
By Fabio Fornaro, Domani AI
A knowledge graph stores knowledge not as running text but as verified facts and their relationships to one another. An AI built on it answers with what's substantiated instead of guessing — and can explain where an answer comes from.
Start here
What is a knowledge graph?
The basics in plain language: nodes, edges, verified knowledge — and why that breaks an AI's habit of hallucinating.
→Knowledge graph vs. RAG vs. chatbot
The honest comparison: what each approach can do, where it hits limits — and when to combine them.
→Knowledge graph & GDPR
How a knowledge graph supports GDPR-compliant AI — and what it doesn't solve automatically.
→When is a knowledge graph worth it?
Honest yes/no criteria: when it pays off — and when a chatbot or RAG is enough.
→Use cases: diagnosis, recommendation, compliance
What a knowledge graph is good for in practice — with industries and the Plant-Doctor case.
→AI without hallucination
Why AIs hallucinate, how a knowledge graph curbs it — and where the honest limit lies.
→30-day pilot
Low-risk entry: prove it on real cases first, then scale.
→KG vs. Google Knowledge Panel
Three things often confused — cleanly separated, plus a guide.
→Example knowledge graph
A mini example with a visualization — how knowledge becomes a reliable answer.
→Knowledge Brain — our offer
How we turn a knowledge graph into your own AI: recognises, diagnoses, recommends. With a configurator.
→What does a knowledge graph cost?
Honest pricing logic: why curation is the cost driver, and the indicative figure it starts at.
→Why Domani AI for knowledge graphs
We've built a knowledge graph in production, not just in theory: for a client in the plant space (Plant-Doctor), verified expert knowledge sits as a graph — the AI recognises, diagnoses and recommends without making things up. That care — curated, verifiable knowledge instead of cobbled-together text — is the difference between "sounds plausible" and "is correct".
“A knowledge graph stores knowledge as verified facts and their relationships — so an AI answers only with what is substantiated instead of guessing.”
“Domani AI built a diagnostic AI on a curated knowledge graph (Plant-Doctor): it recognises, diagnoses and recommends without hallucinating.”
Frequently asked
Is a knowledge graph the same as a chatbot?
No. A chatbot is the surface you ask through. A knowledge graph is the verified knowledge underneath that the answer comes from. A good expert chatbot needs both — otherwise it guesses.
Knowledge graph or RAG — which is better?
It depends. RAG retrieves similar chunks of text and hands them to the AI; a knowledge graph knows verified relationships. Where wrong answers are expensive (diagnosis, law, compliance), the graph is far more reliable — often you combine both.
Doesn't an AI on a knowledge graph hallucinate?
Far less often, because it draws only on stored, verified knowledge and can say "I don't know" instead of inventing. AI never gives absolute guarantees — but the difference in practice is large.
Knowledge that answers — instead of guessing?
Tell us what expert knowledge you have. We'll tell you honestly whether a knowledge graph is the right path.
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