Fabric iq

Microsoft Fabric IQ

Fabric IQ (preview) is a Fabric workload for unifying data across OneLake and organizing it according to your business vocabulary. It exposes data to analytics, AI agents, and applications with consistent semantic meaning and context.

When to Use This Skill

• Creating or managing ontology items in Fabric IQ
• Defining entity types, properties, and relationship types
• Binding data from lakehouses, eventhouses, or semantic models to ontologies
• Generating ontologies from Power BI semantic models
• Configuring Fabric data agents with ontology as a source
• Working with Graph in Microsoft Fabric for traversals and graph queries
• Enabling Fabric IQ tenant settings in the admin portal
• Querying ontology graphs using the preview experience
• Building operations agents that reason across business concepts
• remediate ontology creation, data binding, or agent integration issues
• Automating Fabric IQ items via REST API or PowerShell

Prerequisites

1. A Fabric workspace with a Microsoft Fabric-enabled capacity (F2+ or P1+)
2. Required tenant settings enabled (see tenant-settings.md)
3. Data in OneLake (lakehouse tables), an eventhouse, or Power BI semantic models

Fabric IQ Items Overview

Fabric IQ contains five items that work together:

Item	Purpose	Shared With
Ontology (preview)	Enterprise vocabulary and semantic layer — entity types, relationships, properties, data bindings	IQ only
Graph in Microsoft Fabric (preview)	Native graph storage/compute for nodes, edges, traversals, path finding	Real-Time Intelligence
Fabric data agent (preview)	Conversational Q&A using generative AI, grounded in ontology	Data Science
Operations agent (preview)	AI agent to monitor real-time data and recommend actions	Real-Time Intelligence
Power BI semantic model	Curated analytics model for reporting and DAX	Power BI

Choosing the Right Item

Scenario	Use
Cross-domain consistency, governance, AI agent grounding	Ontology
Relationship-heavy questions (impact chains, shortest paths)	Graph
Trusted KPIs and fast visuals with dimensional modeling	Power BI semantic model
Operational context, stateful twins, what-if simulation	Digital twin builder (Real-Time Intelligence)

Step-by-Step Workflows

Workflow 1: Create an Ontology from OneLake

For the complete walkthrough with all field mappings, see ontology-workflows.md.

1. Navigate to your Fabric workspace and select + New item > Ontology (preview)
2. Name the ontology (letters, numbers, underscores only — no spaces or dashes)
3. Add entity types from the ribbon or canvas
4. Bind static or time series data from OneLake sources
5. Set entity type keys (unique identifier properties)
6. Create relationship types between entity types and bind them to source data
7. Use the preview experience to explore entity instances and the ontology graph

Workflow 2: Generate an Ontology from a Semantic Model

For the complete walkthrough, see ontology-workflows.md.

1. Navigate to your Power BI semantic model in Fabric
2. Select Generate Ontology from the ribbon
3. Choose workspace and name the ontology
4. Verify generated entity types, bindings, and relationship types
5. Configure any incomplete relationship bindings manually

Workflow 3: Connect an Ontology to a Data Agent

For the complete walkthrough, see ontology-workflows.md.

1. Create a Data agent item in your workspace
2. Add the ontology as a knowledge source
3. Add agent instructions (e.g., Support group by in GQL)
4. Test queries in the agent chat to validate semantic grounding

Workflow 4: Validate Tenant Prerequisites

Run the prereq validation script to check your environment:

./scripts/Validate-FabricIQPrereqs.ps1 -TenantId "your-tenant-id"

Key Concepts

Ontology Core Concepts

Concept	Description
Entity type	Represents a real-world concept (e.g., Customer, Truck, Sensor)
Property	A fact about an entity type (e.g., name, email, temperature)
Entity type key	Unique identifier property for entity instances
Relationship type	Semantic connection between entity types (e.g., "drives", "has", "soldIn")
Data binding	Connects ontology definitions to concrete OneLake data sources
Ontology graph	Queryable instance graph built from data bindings and relationships

Data Binding Types

Type	Use Case	Example
Static	Descriptive attributes that change infrequently	Store locations, product catalog
Time series	Timestamped observations in columnar format	Sensor telemetry, temperature readings

Naming Constraints

Element	Rules
Ontology name	Letters, numbers, underscores. No spaces or dashes
Entity type name	1-26 chars, alphanumeric + hyphens + underscores, start/end alphanumeric
Property name	1-26 chars, alphanumeric + hyphens + underscores, unique across entity types for same type

REST API Support

The Fabric REST API supports ontology CRUD operations:

Operation	Supported
Create (without definition)	Yes
Create (with payload/definition)	Yes
Service principal support	Yes
Get	Yes
Update	Yes
Delete	Yes
List	Yes

Use the Fabric CLI for command-line operations:

pip install ms-fabric-cli
fab auth login

remediate

For the full remediate guide, see remediate.md.

Issue	Quick Fix
Unable to create ontology item	Enable all required tenant settings
Graph errors on new ontology	Enable User can create Graph (preview) tenant setting
Data agent 403 Forbidden	Enable Copilot and Azure OpenAI tenant settings
Generated ontology has no entity types	Ensure semantic model tables are visible (not hidden)
Generated ontology has no data bindings	Check semantic model mode — Import mode not supported
Decimal properties return null	Recreate property as Double type
Aggregation queries fail in data agent	Add instruction: Support group by in GQL

References

• Tenant Settings & Prerequisites
• Ontology Workflows (Detailed)
• remediate Guide
• Prereq Validation Script
• Ontology Template
• Microsoft Learn: Fabric IQ Overview
• Microsoft Learn: Ontology Overview
• Sample Data: IQ Samples on GitHub