#ontology

Feb 19, 2026

Beyond the Star Schema: Why Ontologies are the New Requirement for AI-Ready Data

Relational data models weren’t designed for AI or natural language, so while they store data well, they fail to capture business meaning—making accurate, trustworthy AI querying difficult. Fabric IQ addresses this by introducing an ontology layer that sits above existing data, explicitly modelling business concepts, relationships, and rules without requiring data to be moved or reshaped. Combined with Fabric Data Agents, this enables AI to navigate defined meaning rather than guess it, improving query accuracy, consistency, and scalability for enterprise analytics.

Mar 26, 2026

Ontologies: What Do I Mean vs What Do I Want?

The article argues that prompt‑heavy approaches to natural language querying are fundamentally brittle because they force LLMs to manage meaning, preference, and logic simultaneously—something they are not designed to do, leading to ambiguity and inconsistency in enterprise analytics. It positions Fabric Data Agents as a more structured alternative, where instructions are deliberately scoped to behaviour and intent, while meaning is externalised—reducing the fragility seen in traditional prompt stacks. Fabric IQ is presented as the key enabler of this shift, providing ontologies as a first‑class semantic layer that lets agents move from inferring business meaning to reliably navigating explicit definitions, making natural language over enterprise data more consistent and scalable.

Apr 25, 2026

The Agents: Data Agent & Operations Agent in Practice

A solutions architect's deep dive into the two agents powering Fabric IQ - Data Agents and Operations Agents. Covers the execution chain, governance model, billing math, configuration levers, and the combined architecture where one agent detects and the other explains over a shared Ontology.

Ontology with Data Agent

This project provides a complete, end‑to‑end walkthrough for building a Microsoft Fabric Ontology demo—either by installing a ready‑made healthcare example or by creating your own custom ontology, lakehouse tables, demo data, and graph queries. It shows how to use tools like VS Code, GitHub Copilot, Semantic Link Labs notebooks, and the Fabric Ontology Playground to generate RDF models, orchestrate table creation, build a graph database, and construct multi‑hop GQL queries that highlight the strengths of graph reasoning. It also guides you through creating a Fabric Data Agent powered by your ontology, including AI instructions and sample prompts, enabling a fully functional demo environment in under an hour.

Apr 14, 2026

Fabric Ontology with Data Agent

AI built my Fabric Ontology demo in under an hour! Thanks to GitHub Copilot, I have notebooks for loading Lakehouse tables with demo data, building a Fabric Ontology, and creating instructions for a Fabric Data Agent to query the whole thing. Visit my GitHub repo to see the prompts I used, how Ontology Playground helped, and the skills files that were created along the way.