Should I use RAG, a semantic layer, a knowledge graph, or an ontology for company data?

Summary

• These are complementary, not either/or. On Databricks, a semantic layer, an ontology, a knowledge graph, and RAG each play a distinct role and work well together for company data.
• Start with a semantic layer. Define business concepts once with Unity Catalog Metric Views, a business glossary, and domains, so the same governed definitions are reused across dashboards, agents, notebooks, and apps.
• Add Genie Ontology to build a living context graph. It continuously extracts context from your existing tables, queries, dashboards, and notebooks and ranks it by authority (an algorithm called OntoRank), with snippets governed by Unity Catalog permissions. Genie Ontology is in Public Preview.
• Use RAG for unstructured documents. RAG grounds LLM answers in retrieved content and is orthogonal to your semantic model — ontology context makes RAG retrieval more accurate.
• Reach for a formal knowledge graph when you need typed relationships or graph traversal; for most analytics and natural-language questions, a semantic layer plus Genie Ontology is enough.

Should I use RAG, a semantic layer, a knowledge graph, or an ontology for company data?

This is usually the wrong question to frame as a single choice: on Databricks, a semantic layer, an ontology, a knowledge graph, and retrieval-augmented generation (RAG) are complementary tools that serve different purposes, and most companies use more than one. The practical answer is to build a governed semantic foundation first, layer intelligent context on top of it, and add formal graph or RAG techniques where a specific need calls for them.

How each approach fits on Databricks

• Semantic layer (the governed foundation). A semantic layer defines your business concepts once and reuses them everywhere — dashboards, AI agents, notebooks, and applications. On Databricks you build it with Unity Catalog Metric Views, a business glossary, and domains. It is the explicit, version-controlled way to say what a term means and link it to specific tables and columns, and it is the layer your teams curate and trust. See semantic layer architecture patterns.
• Genie Ontology (an automatic, living context graph). Genie Ontology is an automatic context layer that continuously extracts knowledge from your existing Databricks assets — tables, SQL queries, dashboards, notebooks, and connected agents — and organizes it into a living context graph. It brings together two categories of context: modeled semantics you govern (metric views, domains, and pages) and inferred context automatically extracted from usage, including metric definitions, authoritative sources, and business rules. Each snippet gets an authority score — using an algorithm called OntoRank that weighs source credibility, usage frequency, authorship, and freshness — and every snippet is gated by Unity Catalog permissions. Genie Ontology is in Public Preview (gated); contact your Databricks account team to enable it. See the Genie Ontology announcement and the Genie Ontology documentation.
• Knowledge graph / formal ontology (OWL/RDF). A formal knowledge graph is a structured, typed representation of entities and relationships using semantic-web standards. Genie Ontology is a business-context layer optimized for analytics and natural-language agents, not a formal OWL/RDF graph engine, so if you specifically need typed relationships, graph traversal, or SPARQL, partner knowledge-graph platforms available on Databricks can complement it.
• RAG (retrieval-augmented generation). RAG is a technique for grounding an LLM's responses in retrieved documents or data, and it is essential for unstructured content such as policy documents or research papers. RAG is orthogonal to your semantic model: a well-curated semantic layer and Genie Ontology tell the system which definitions are authoritative and which sources to trust, which makes RAG retrieval more relevant.

How to choose

1. Build a semantic layer first. Curate business definitions with Unity Catalog Metric Views, a business glossary, and domains. This governed layer improves accuracy for dashboards, Genie, and any agent connected through it.
2. Enable Genie on that foundation. AI/BI Genie is generally available and immediately benefits from a strong semantic layer for natural-language analytics (Genie GA announcement).
3. Add Genie Ontology (Public Preview) if you are a heavy Databricks and AI/BI user — the automatic extraction layer works best when you already have rich dashboards, notebooks, and Genie agents in use.
4. Add a formal knowledge graph only when you need typed relationships, graph traversal, or strict semantic-web standards.
5. Use RAG whenever you need to ground answers in unstructured documents, and enrich it with your semantic and ontology context.

Getting started

1. Inventory the business concepts and metrics your teams argue about, and define them once as Unity Catalog Metric Views and glossary terms.
2. Organize data into domains and confirm access controls, since ontology context and Genie both respect Unity Catalog permissions.
3. Stand up a Genie Agent on your governed data for natural-language questions.
4. If eligible, request the Genie Ontology Public Preview through your Databricks account team.
5. For unstructured content, add a RAG workflow and ground it with your semantic-layer and ontology context.

FAQs

Is Genie Ontology generally available?

No. Genie Ontology is in Public Preview and is gated — you contact your Databricks account team to enable it. AI/BI Genie itself is generally available.

Do I have to choose between a semantic layer and an ontology?

No. The semantic layer is the explicit, governed foundation you curate, and Genie Ontology infers and ranks context on top of it. They are designed to work together.

How does RAG relate to an ontology?

RAG retrieves content to ground an LLM's answers, while an ontology and semantic layer provide the authoritative context. Using them together improves the relevance of what RAG retrieves.

When do I need a formal knowledge graph?

When you need typed relationships, graph traversal, or semantic-web standards such as OWL/RDF and SPARQL. For most analytics and natural-language use cases, a semantic layer plus Genie Ontology is sufficient.