October 2026 – The Data Community

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.

Tutorial Part 0: Introduction and Environment Setup - Microsoft Fabric | Microsoft Learn

(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.

Microsoft Fabric October & November 2025 Update | element61

(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.

Tutorial part 3: Preview the ontology - Microsoft Fabric | Microsoft Learn

(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.

Add Relationship Types - Microsoft Fabric | Microsoft Learn

(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.

Use preview experience - Microsoft Fabric | Microsoft Learn

(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

This is a quick article that describes what an ontology is and why ontologies are important.

What is an Ontology?

In data management, an ontology is a formal representation of knowledge that defines the concepts within a particular domain, their properties, and the relationships between them. It provides a shared understanding of what data means, how different entities relate to one another, and how systems can interpret those relationships consistently.

While a database schema defines how data is structured and stored, an ontology goes a step further by describing the meaning of the data and the rules governing its relationships.

What does an Ontology consist of?

An ontology typically consists of four key elements:

Entities or concepts: The things being described, such as Customers, Products, Orders, and Employees.
Properties: The characteristics of those concepts, such as a customer’s name, a product’s price, or an employee’s department.
Relationships: How concepts connect to one another, such as a Customer placing an Order or an Employee managing a Department.
Rules and constraints: Conditions that define how concepts behave or relate, such as an Order containing at least one Product.

Consider a retail business. Its ontology might define that a Customer places an Order, an Order contains Products, and each Product belongs to a Product Category. These relationships help systems understand the business beyond individual database tables.

Ontology vs. Data Model vs. Semantic Model vs. Knowledge Graph

Although these concepts are related, they serve different purposes.

Concept	Primary Purpose	Example
Data Model	Defines the structure and organization of data.	Customer and Order tables connected by a foreign key.
Ontology	Defines concepts, meanings, relationships, and rules within a domain.	A customer places an order, which contains products.
Semantic Model	Organizes data around business concepts, relationships, measures, and definitions to support consistent analysis and reporting.	A Power BI model connecting Customers, Products, and Sales, with a standardized Total Revenue measure.
Knowledge Graph	Represents entities and their relationships as connected data.	A graph showing customers, orders, products, and their connections.

An ontology can provide the semantic foundation for a knowledge graph, while a data model defines how information is organized in a particular system. A semantic model builds on data structures and business definitions to make information easier to query, analyze, and interpret consistently.

For example, a Power BI semantic model may define a standardized Revenue measure, establish relationships between sales and product tables, and organize fields into user-friendly business categories. An ontology could extend this understanding by formally defining what a Customer or Product represents and how those concepts relate across multiple business systems.

Why are Ontologies important?

Ontologies are increasingly valuable in modern data environments because they help organizations:

Improve data integration: Connect information across systems that use different structures or terminology.
Establish consistent business definitions: Ensure that concepts such as Customer, Revenue, or Active Account have a shared meaning.
Enable semantic search: Allow users and applications to find information based on meaning and relationships rather than just matching keywords.
Support artificial intelligence: Give AI systems structured domain knowledge that can help them interpret questions, identify relationships, and produce more contextually relevant responses.
Improve data discovery: Help analysts and business users understand how datasets and business concepts relate to one another.

For example, an AI assistant answering the question, “Which customers purchased products in our premium category?” could use an ontology to understand the relationships between customers, purchases, products, and product categories—even when the underlying information is distributed across multiple systems.

Ontologies and the future of data

As organizations adopt knowledge graphs, semantic models, and AI-powered applications, ontologies are becoming an important component of data architecture. They help bridge the gap between raw data and business meaning, allowing both people and machines to interpret information more consistently.

The key takeaway: A data model explains how data is organized, an ontology explains what the data represents and how its concepts relate, a semantic model makes data meaningful and usable for business analysis, and a knowledge graph represents entities and their relationships as connected information. Together, these concepts help organizations build more connected, intelligent, and AI-ready data environments.

Thanks for reading!