Learn
Ontology & Fabric IQ
From what counts as one thing, to an AI agent that can reason over your whole business.
Start here
The concepts read in order, and the lab is one build you keep open and advance as you go. Read a module, then open the lab and build the step it points to. By the end you have modeled an ontology, materialized its graph, and grounded an agent on it, by hand.
Read in order
Each page is one idea with a diagram, a small interactive, and a check-yourself. The pages bridge into each other, so top to bottom is the intended path.
Keep one lab open
The lab is a single build, not one per module. Open it once and advance the same build. Every concept links to the exact lab step where you practice it.
Mark what you finish
Each page has a mark-as-learned toggle. It saves in your browser, so this page shows how far you have gotten.
Before you build anything
Open the lab and run the guided tour once, so you see the whole map before you make a piece of it. Then load the Lakeshore cold-chain demo for real data, and build in manual mode. Manual is slower and teaches more than auto.
Open the Ontology Lab →Your progress
Tick a concept here, or mark it learned on its page, and the meter fills in. Saved in this browser.
The Gap
Why tables and semantic models hit a wall for AI agents, and what closes it.
The map is not the territory
OverviewYour data is a map built for the questions you already had. An AI agent needs to go where it was never drawn.
Fabric IQ & the intelligence layer
OverviewHow Fabric IQ turns a data estate into a shared, reasoning-ready model.
OneLake: the unified foundation
OverviewOne governed lake so every concept binds to the same source of truth.
Things
The first modeling decision: what counts as one kind of thing.
Entity
OntologyA concept declared above your tables so every source means the same thing.
Property
OntologyA named fact about an entity, with a declared type and unit.
Identity & keys
OntologyWhat makes two rows the same thing — the entity key.
Entity instance
OntologyA concrete occurrence, with provenance and the time it was true.
Connections
How things relate — and how meaning travels across them.
Grounding
Connecting the model to real, governed, quality-checked data.
The Graph
Relationships become first-class, queryable, and traversable.
Knowledge graph
GraphInstances as nodes, relationships as edges — context made explicit.
Labeled property graph
GraphThe graph model Fabric uses: labels and properties on nodes and edges.
Asserted vs. derived edges
GraphFacts you stored vs. links a rule inferred.
Cost & materialization
GraphA graph is materialized and metered: you pay for a floor and for uptime, asked or not.
Graph query (GQL)
GraphAsk relationship questions by pattern — no SQL joins.
The Semantic Layer
Assemble the whole — and see how ontology and semantic models relate.
Ontology
OntologyCatalog + bindings + graph + query, assembled into one shared model.
Power BI semantic model
Power BI semantic modelMeasures, hierarchies, dimensions — the curated analytics layer.
Semantic model vs. ontology
Power BI semantic modelWhen to reach for each — and generating one from the other.
Agents & Action
Both people and AI reason over the model — and act on it.
NL2Ontology
Data agentTurn a business question into a structured, grounded query.
Grounded data agent (GraphRAG)
Data agentAnswers grounded in the ontology instead of guessed from tables.
When graphs lose
Data agentThe graph advantage is regime-dependent: on simple fact retrieval, plain retrieval matches it for a fraction of the tokens.
Operations agent
Operations agentMonitor live data, detect anomalies, trace impact across the graph.
Actions & rules
Operations agentGoverned actions the agent can take — the last mile of the loop.
Who can see it, and who acts
GraphA viewer sees the whole graph, and the agent acts as its creator: two trust models meeting at one item.
Breadth: Plan & Integrations
Speak to the whole product — planning and where the ontology surfaces.