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This tutorial shows how to create your first ontology (preview) in Microsoft Fabric. You create ontology elements manually and with the help of the ontology agent, and bind them to data from multiple OneLake data sources. You enrich the ontology with additional metadata and information, and explore the completed ontology in the visual experience as well as through ontology agent queries.
Important
This feature is in preview.
The example scenario for this tutorial is a fictional company called Lakeshore Retail. Lakeshore is a retail ice cream seller that keeps data on sales and refrigerator streaming data. In the tutorial, you create entity types like Store, Products, and Sale. You bind streaming data like refrigerator temperature from an eventhouse, and answer questions like: "What is the top product by revenue across all stores?"
Prerequisites
A workspace with a Microsoft Fabric-enabled capacity. Use this workspace for all resources you create in the tutorial.
The Users can create ontology (preview) items and Users can create Fabric items settings enabled on your Fabric tenant.
A Fabric administrator can enable the settings in OneLake catalog > Govern > Configurations > Tenant settings:
Scenario
Lakeshore Retail is a multi-region retailer that sells general merchandise, perishable goods, and frozen products through stores supplied by regional distribution centers. Store operations teams need to maintain product availability without increasing waste, while frozen products must remain within safe storage temperatures throughout the retail cold chain.
This tutorial works toward one concrete operational outcome: Which high-priority stores in the West have frozen products below safety stock, a recent refrigeration-temperature exception, and on-shelf availability below target? For each store, show the next inbound shipment and its expected arrival.
With a robust ontology, the ontology agent can answer this question. Design an ontology that helps the agent understand that a frozen product is a type of product, what below safety stock and temperature exception mean, how products are stocked at stores, which refrigeration units operate at each store, how inbound shipments connect distribution centers to stores, and how the governed Power BI measure for on-shelf availability should be calculated.
The ontology in this tutorial connects operational signals, business definitions, governed metrics, and business relationships so that an agent can explain where intervention is needed and why.
Ontology
The Lakeshore Retail elements in this tutorial comprise the following ontology. Review the diagram to understand the entity types and relationships that are involved in the scenario.
Download sample data
Download the contents of the New experience folder from ontology samples in GitHub: Ontology samples.
The sample data includes a set of CSV files containing static entity details about the Lakeshore Retail scenario and streaming data from its refrigerators, as well as a Power BI semantic model containing sales data.
Prepare the lakehouse
Follow these steps to prepare the sample tutorial data in a lakehouse.
Start in your Fabric workspace. Use the + New item button to create a new Lakehouse item.
For the lakehouse Name, enter LakeshoreStaticDataLH. For Location, select your workspace. Keep the Lakehouse schemas checkbox selected (default) and select Create.
The new lakehouse opens when it's ready. From the lakehouse ribbon, select Get data > Upload files.
Upload all except one of the sample CSV files to your lakehouse. Don't upload refrigeration_telemetry.csv (you upload this file to eventhouse instead in a later step), or SalesReport.pbix (you import this data through the ontology agent in a later step).
Expand the Files folder in the Explorer to view your uploaded files. Next, load each file to a delta table.
For each file, select ... next to the file name, then Load to Tables > New table.
Continue through the table creation dialog, keeping the default settings.
The lakehouse looks like the following image when you're done. The default table names reflect the file names in all lowercase.
Prepare the eventhouse
Follow these steps to upload the device streaming data file to a KQL database in an eventhouse.
In your Fabric workspace, select + New item to create a new eventhouse called LakeshoreTelemetryDataEH. Fabric creates a default KQL database with the same name.
The eventhouse opens when it's ready. Open the KQL database by selecting its name in the explorer.
Next, create a new table called RefrigerationTelemetry that uses the refrigeration_telemetry.csv sample file as a source.
In the menu ribbon, select Get data > Local file.
Create a New table called RefrigerationTelemetry and browse for the refrigeration_telemetry.csv file that you downloaded earlier.
Continue through the table creation dialog, keeping the default settings.
When you're done, the KQL database shows the RefrigerationTelemetry table with data:
Upload the semantic model
Finally, upload the sample semantic model to your workspace.
In your Fabric workspace, select Import > Report, Paginated Report or Workbook > From this computer.
Find the SalesReport.pbix file that you downloaded and select Open.
The report is visible in your workspace, and a semantic model is created with the same name.
Next steps
Now your sample scenario is set up in Fabric. Next, create an ontology (preview) item and add elements to it that connect to these data sources: Create an ontology.