What Is an Ontology in Microsoft Fabric?

An ontology provides a formal definition of business concepts, their properties, and the relationships between them. In a modern data platform, it helps connect technical data assets to business meaning, making information easier for analysts, applications, and AI systems to interpret.

In Microsoft Fabric, it is important to distinguish between building an ontology as a conceptual design and implementing it using Fabric’s data and semantic capabilities. A Power BI semantic model, for example, can define business entities, relationships, measures, and terminology, but it is not automatically a formal ontology.

Fabric provides the data engineering, analytics, and semantic modeling capabilities that can serve as the foundation for an ontology-driven solution.

Steps to Create an Ontology in Microsoft Fabric

Microsoft Fabric now includes an Ontology (preview) item that lets you define business entities, properties, relationships, and bindings to underlying data. You can create one from an existing semantic model or build it directly from data in OneLake.

(from Microsoft Learn)

1. Prepare your data

Organize relevant business data in a Fabric lakehouse or create a Power BI semantic model with appropriate tables, columns, relationships, and measures.

(from Microsoft Learn)

2. Create the ontology

To reuse an existing semantic model, open its overview page and select Generate Ontology. Alternatively, select + New item in your workspace and choose Ontology (preview) to build one from scratch.

(from Microsoft Learn)

3. Define entities and relationships

Create business entities such as Customer, Product, Store, and Sale. Define their properties, select appropriate entity keys, and establish relationships such as a Customer placing an Order.

(from Microsoft Learn)

4. Bind the ontology to your data

Map entity properties and relationships to the appropriate source tables and columns. This connects business definitions to actual enterprise data without requiring the ontology to duplicate that data.

(from Microsoft Learn)

5. Validate and use the ontology

Verify entity keys, property mappings, and relationships. You can then explore connected data through supported ontology experiences, including graph visualization and natural-language queries using Fabric data agent capabilities.

Example: A Retail Ontology

A retailer could define the following business relationships:

Customer

places

Sale / Order

contains

Product

belongs to

Product Category

With the relationships and data bindings configured, an AI-powered experience could use this shared business context to help answer questions such as, “Which customers purchased products in our premium category?”

Important Considerations

• Preview status: As of October 2026, Fabric’s Ontology item is in preview, so capabilities and user interfaces may change.
• Permissions and capacity: Check that your tenant has ontology functionality enabled, your workspace uses a supported Fabric capacity, and you have the required workspace and semantic model permissions.
• Quality matters: Review generated entities and relationships rather than assuming the automatically created ontology is complete.
• Start with business meaning: Agree on entity definitions, identifiers, and relationships before expanding the ontology across multiple data domains.

Conclusion

Creating an ontology in Microsoft Fabric helps organizations move beyond storing and reporting data toward representing the meaning of their business information. By building on existing semantic models or binding directly to OneLake data, organizations can create a reusable business context that supports connected analytics and AI applications.

Checkout the official, complete walkthrough on Microsoft Learn … https://learn.microsoft.com/en-us/fabric/iq/ontology/tutorial-1-create-ontology

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