Microsoft Fabric IQ Ontology Step by Step
Struggling with AI agents that hallucinate or give wrong answers from your data? The problem usually isn't your model — it's that your tables and schemas were built for machines, not for business meaning. In this video I walk you through Microsoft Fabric IQ and the Ontology feature end-to-end, so your data finally speaks the language of your business and your AI agents stop guessing.This is a complete A-to-Z, beginner-friendly guide. We start with the theory of Microsoft Fabric IQ and where it sits in the wider Microsoft IQ family (Work IQ, Foundry IQ, Web IQ, and Fabric IQ), then move into a full hands-on demo: building a Power BI semantic model on a Lakehouse in OneLake, generating an Ontology, defining entity types, properties, and relationships, running visual graph queries, and finally consuming the ontology inside a Fabric Data Agent using natural language — with zero SQL.
Chapters:
00:00 Introduction & Full Agenda — A to Z of Ontologies in Fabric
01:30 What Is Microsoft Fabric IQ? The Enterprise Intelligence Layer
02:05 Work IQ vs Foundry IQ vs Web IQ vs Fabric IQ
04:19 Why Choose Fabric IQ? Key Benefits Explained
04:55 The 3 Layers of Fabric IQ (OneLake, Semantic Model, IQ)
05:54 Ontology Feature in Preview & Where It Fits
07:32 Without Fabric IQ vs With Fabric IQ — Solving AI Hallucination
10:49 What Is an Ontology? Entities, Properties & Relationships
13:21 Required Power BI Admin Center Tenant Settings
14:10 Enabling Copilot & Azure OpenAI for the Fabric Data Agent
15:24 Demo: Lakehouse Setup with Product, Store, Sales & Freezer Tables
16:38 Building a Semantic Model on the Lakehouse
17:57 Establishing Fact-to-Dimension Relationships
19:09 Generating the Ontology from the Semantic Model
20:06 Exploring Entity Types, Properties & the Ontology Graph
22:44 Renaming Entities for Business Context (Store, Product, Sales)
24:47 Defining the Entity Type Key (Primary Key) for the Fact Table
28:13 Naming Ontology Relationships (Sold, etc.)
31:49 Building & Running Visual Graph Queries (Add Node, Filters, Card View)
35:35 Using the GQL Query Code Editor
35:58 Consuming the Ontology in a Fabric Data Agent
36:53 Creating the Retail Ontology Data Agent
37:29 Adding the Ontology as a Data Source & Agent Instructions
38:43 Testing Natural Language Questions (Top 5 Products by Revenue)
39:56 Visualizing Answers as a Bar Graph
41:20 End-to-End Recap: Zero-SQL Natural Language Querying
43:00 Key Takeaways & Wrap-Up
🔗 Resources Mentioned
Microsoft Learn — What is Fabric IQ?: https://learn.microsoft.com/fabric/iq/overview
Microsoft Learn — What is Ontology (preview)?: https://learn.microsoft.com/fabric/iq/ontology/overview
Fabric IQ landing: https://aka.ms/FabricIQ
(Ontology is in preview — always check the latest official Microsoft docs for current availability)
🔁 Why Ontology over plain semantic models?
A semantic model is technical — tables, columns, relationships, and DAX measures built for reporting. An ontology is conceptual — it describes what things are and how they connect, in business language both humans and AI agents can read. One ontology concept can map across many tables and sources, giving agents a single, governed source of meaning so answers stay consistent and explainable.
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