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Generating an ontology (preview) 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 them from lakehouse tables. When your data is in a semantic model, you can generate an ontology directly from that semantic model.

Important

This feature is in preview.

Process overview

Ontology generation automatically creates the following artifacts:

  • A new ontology (preview) item in your Fabric workspace, with a name that you choose.
  • Entity types in the ontology that match the tables in your semantic model.
  • Static properties on each entity type based on the columns in your tables, and data bindings that link your data rows to these properties.
  • Relationship types between entity types that follow relationships defined in the semantic model.

After generating an ontology, complete these actions manually:

  • Bind time series data to entity types. Properties for time series data aren't created automatically.
  • Review entity type keys and add any that are missing, especially for multi-key scenarios.
  • Bind relationship types to data.
  • Review the entire ontology to make sure entity types, their properties and data bindings, and relationships are complete.

Support for semantic model modes

This section describes feature support in ontology (preview) for different semantic model modes. For more information about semantic models and their modes, see Power BI semantic models in Microsoft Fabric.

Feature Import mode Direct Lake mode DirectQuery mode
Generating entity type definitions Supported Supported Supported
Generating property definitions Supported Supported Supported
Generating relationship definitions Supported Supported Supported
Generating entity type bindings to data sources Not supported Supported ONLY when backing lakehouse is in a workspace with inbound public access enabled (otherwise, the ontology item is created successfully but that entity type has no data bindings) Not Supported
Generating relationship type bindings to data sources Not supported Supported ONLY when primary key is identified (the primary key is used as the entity type key for the ontology) Not Supported
Querying data using bindings to data sources Not supported Supported (without measures and calculated columns) Not Supported

Other limitations

Other limitations for generating ontology from a semantic model are organized by category below.

Semantic model service limitations

Semantic models used for ontology generation are subject to general limitations of semantic models in the Power BI service. Some examples of relevant limitations are semantic model size considerations and XMLA endpoint limitations.

Lakehouse tables

  • Ontology only supports managed lakehouse tables (located in the same OneLake directory as the lakehouse), not external tables that show in the lakehouse but reside in a different location.

  • The ontology graph does not support delta tables with column mapping enabled. Column mapping can be enabled manually, or is enabled automatically on lakehouse tables where column names have certain special characters, including ,, ;, {}, (), \n, \t, =, and space. It also happens automatically on the delta tables that store data for import mode semantic model tables.

  • Fabric Graph doesn't currently support the Decimal type. As a result, if you generate an ontology from a semantic model with tables that include Decimal type columns, you see null values returned for those properties on all queries.

    Note

    Decimal is different from the floating-point Double type, which is supported. Decimal is a fixed-precision numeric type that is most commonly used for representing monetary values.

Workspace

You can't generate an ontology from a semantic model in the default Fabric workspace My workspace. Make sure the semantic model is in a different workspace.

Troubleshooting

For troubleshooting tips related to semantic model ontology generation, see Troubleshoot ontology (preview).

Next steps

For an example of this process, see Ontology (preview) tutorial part 1: Create an ontology.

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