Revised summary

Author: theresa-i

learn-pr/wwl-data-ai/understand-fabric-iq-fundamentals/includes/6-summary.md

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-Microsoft Fabric IQ provides a way to define business vocabulary in ontologies and bind them to data sources. Instead of teams building separate interpretations of business concepts, you define entity types, properties, and relationships once in an ontology, then bind them to data sources. This approach provides a semantic layer that data agents and Graph in Microsoft Fabric can use.
+Microsoft Fabric IQ lets you define business vocabulary once in ontologies, enabling natural language queries and graph visualization of your data. In this module, you learned what Fabric IQ is, how to access it, and how it fits within Microsoft Fabric's data platform.
 
-You explored how Fabric IQ brings together complementary components: ontology items establish your business vocabulary, data agents enable natural language queries, and Graph visualizes and analyzes relationships. Each component serves a distinct role while working together through the unified ontology foundation.
+You explored the build-bind-query workflow for creating ontologies: defining entity types and relationships, binding them to lakehouse tables and eventhouse streams, and querying them through Graph or data agents. You also saw how to generate ontologies from existing Power BI semantic models or build them from scratch using OneLake data.
 
-The ontology modeling paradigm shifts focus from use-case-driven modeling to concept-driven modeling. Traditional analytical data modeling starts with specific reporting and analytics needs—designing tables optimized for particular queries. Ontology modeling inverts this by starting with core business concepts and their relationships, then binding those definitions to data sources. Entity types provide reusable conceptual definitions, properties standardize terminology across tables, and relationships make connections explicit rather than buried in join logic. Data binding creates a semantic layer that references actual data without copying or moving it—queries execute against source systems (lakehouses, eventhouses) at query time.
+You examined four components that work together: ontology items define your business vocabulary, data agents answer natural language questions across multiple data sources, Graph in Microsoft Fabric visualizes and analyzes relationships, and Power BI semantic models can generate initial ontology structures.
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+Finally, you learned how ontology modeling differs from traditional analytical modeling. Instead of starting with specific reporting needs and designing tables optimized for queries, ontology modeling starts with core business concepts and their relationships. This concept-driven approach creates reusable definitions that both data agents and Graph can use, enabling business users to explore data using familiar terminology.