Ontology (preview) tutorial part 1: Create an ontology

Generating an ontology from a semantic model

A semantic model in Fabric is a logical description of a domain, like a business. Semantic models hold information about your data and the relationships among that data. You can create semantic models from lakehouse tables. When your data is represented in a semantic model, you can generate an ontology directly from that semantic model. For more information, see Generating an ontology (preview) from a semantic model.

In this tutorial step, you generate an ontology from the sample semantic model that you set up in the previous step. Then, you verify and complete the ontology.

Generate ontology

1. Go to the RetailSalesModel semantic model in Fabric. From the top ribbon, select Generate Ontology.

   

2. Select your Workspace and enter RetailSalesOntology for the Name. Select Create.

   

   Tip

   Ontology names can include numbers, letters, and underscores. Don't use spaces or dashes.

The ontology (preview) item opens when it's ready.

Next, review the entity types, data bindings, and relationships that the semantic model generates. In the following sections, you make a few edits to complete the ontology configuration, and verify that generated items are correct.

Verify entity types

Entity types represent types of objects in a business. The Entity Types pane lists all three entity types in the ontology, named after the data tables:

• factsales
• dimstore
• dimproducts

Rename entity types

Follow these steps to rename each entity type to a friendlier name.

1. Select the entity type.

2. In the Entity type configuration pane, select the edit icon next to the Entity type name.

   

   Enter the new name from the following table.

   Old name	New name
   factsales	SaleEvent
   dimstore	Store
   dimproducts	Products Note: Make sure to use the plural form Products, to avoid conflict with the GQL reserved word PRODUCT.

When you finish renaming all the entity types, they look like this (they might be listed in a different order).

Verify properties

To view the properties of an entity type, select it from the Entity types pane. This action opens the Entity type configuration pane, where the entity type's properties are listed on the Properties tab.

Verify that each entity type has the properties described in the following table.

Here's an example of what entity type properties look like.

Add SaleEvent key

Each entity type has an entity type key that represents a unique identifier for each record of ingested data. You can select string and integer columns from your source data as the entity type key. Together, the columns you select uniquely identify a record.

The SaleEvent entity type doesn't have a key that was imported from the source data, so you need to add it manually.

1. Open the SaleEvent entity type.

2. In the Entity type configuration pane, select Add entity type key.

   

3. Select SaleId.

4. When you save the key, it looks like this:

   

Verify bindings

To view data bindings for an entity type, look in the Entity type configuration pane and switch to the Bindings tab. Data bindings connect an entity type to a data source so that you can create instances of the entity type and populate them with data.

Verify that each entity type is successfully bound to the data sources described in the following table.

Here's an example of what the bindings look like.

Verify and configure relationship types

Finally, verify the relationship types between entity types. Relationship types represent how entity types are related to each other in a business context. The relationship types that the import process brings in from the semantic model are defined, but not fully configured and bound to data.

Select the SaleEvent entity type to display it and its relationship types on the configuration canvas.

Select each of the relationship types and update its details to match the following table.

Here's an example of what an updated relationship type looks like.

Building an ontology from OneLake

When your data is stored in OneLake, you can build an ontology from the OneLake data tables.

The manual process involves these steps:

• Create an ontology item
• Create entity types
• Create data bindings for the entity types
  • Select entity type keys
• Create relationship types between entity types, and bind them to source data

In this tutorial step, you build an ontology from the sample OneLake data that you set up in the previous step.

Create ontology (preview) item

1. In your Fabric workspace, select + New item. Search for and select the Ontology (preview) item.

   

2. Enter RetailSalesOntology for the Name of your ontology and select Create.

   Tip

   Ontology names can include numbers, letters, and underscores. Don't use spaces or dashes.

The ontology opens when it's ready.

Next, create entity types, data bindings, and relationships based on data from your lakehouse tables.

Create entity types and data bindings

First, create entity types. Entity types represent types of objects in a business. This step has three entity types: Store, Products, and SaleEvent. After you create the entity types, create their properties by binding source data columns from the OntologyDataLH lakehouse tables.

Add first entity type (Store)

1. From the top ribbon or the center of the configuration canvas, select Add entity type.

   

2. Enter Store for the name of your entity type and select Add Entity Type.

3. The Store entity type is added to the configuration canvas, and the Entity type configuration pane is visible.

   

4. To create entities from existing source data, switch to the Bindings tab. Select Add data to entity type.

   

5. Next, choose your data source.

   1. Select the OntologyDataLH lakehouse and select Connect.
   2. Select the dimstore table and select Next.

   

6. Configure a static data binding for the following properties.

   1. For Binding type, don't change the default selection of Static.
   2. Under Bind your properties, the columns from the dimstore table populate automatically. The Source column side lists their names in the source data, and the Property name side lists their corresponding property names on the Store entity type within ontology. Don't change the default property names, which match the source column names.
   3. Select Save.

   

7. Back in the Entity type configuration pane, the data binding is visible. Next, select Add entity type key.

   

8. Select StoreId as the key property and select Save.

   

Now the Store entity type is ready. Continue to the next section to create the remaining entity types.

Add other entity types (Products, SaleEvent)

Follow the same steps that you used for the Store entity type to create the entity types described in the following table. Each entity has a static data binding with the default columns from its source table.

When you're done, you see these entity types listed in the Entity Types pane.

Create relationship types

Next, create relationship types between the entity types to represent contextual connections in your data.

Store has SaleEvent

1. Select Add relationship from the menu ribbon.

   

2. Enter the following relationship type details and select Add relationship type.

   1. Relationship type name: has
   2. Source entity type: Store
   3. Target entity type: SaleEvent

   

3. The Relationship configuration pane opens, where you can configure additional information. Enter the following details (some fields become visible based on other selections) and select Create.

   1. Source data: Select your tutorial workspace, the OntologyDataLH lakehouse, and the factsales table. This table in the source data can link Store and SaleEvent entities together, because it contains identifying information for both entity types. Each row in this table references a store and a sale event by ID.
   2. Source entity type > Source column: Select StoreId. This setting specifies the column in the relationship source data table (factsales > StoreId) whose values match the key property defined on the Store entity (dimstore > StoreId). In the tutorial data, the column name is the same in both tables.
   3. Target entity type > Source column: Select SaleId. This setting specifies the column in the relationship source data table whose values match the key property defined on the SaleEvent entity. In this case, the relationship data source and the entity data source both use the factsales table, so you're selecting the same column.

   

Now the first relationship is created, and bound to data in your source table. Continue to the next section to create another relationship type.

Products soldIn SaleEvent

Follow the same steps that you used for the first relationship type to create the relationship type described in the following table.

When you're done, you have two relationships targeting the SaleEvent entity type. To see the relationships, select the SaleEvent entity type from the Entity Types pane. You see its relationships on the configuration canvas.