No, an ontology is not just another word for a semantic model, although the two overlap. A semantic model maps business terms like revenue, customer, and region onto tables so every report calculates them the same way. An ontology defines what the things in a domain are, how they relate, and which rules they obey, in a formal language that software can reason over.
Why do people treat ontology and semantic model as the same word?
Because in the broadest sense, an ontology is one kind of semantic model. Taxonomist Heather Hedden makes this point in Taxonomies and Ontologies as Semantic Models (August 2024): both structures carry meaning rather than strings of text, so “semantic” fits them both. In that loose usage, any model that encodes meaning is a semantic model, and an ontology is the richest member of the family.
The data stack uses the phrase far more narrowly. In November 2023, Microsoft renamed Power BI datasets to “semantic models,” a change BI author Chris Webb called surprisingly uncontroversial but disruptive for books, courses, and users. The object itself did not change. It was still tables, relationships, and DAX measures built for reports. dbt uses the same term for the YAML block that feeds its metrics engine.
So “semantic model” now means two things at once. To a knowledge engineer it is a broad category that includes ontologies. To an analytics engineer it is a metrics layer for BI. Vendors blur the line further: Microsoft Fabric IQ can generate an ontology from an existing Power BI semantic model, which makes the two look like versions of one thing.
Practitioners notice. An r/dataengineering thread titled “buzzword bingo” asks exactly this question. In practice, the BI sense of semantic model and an ontology do different jobs, answer different questions, and are maintained by different people.
What is an ontology, exactly?
An ontology is a formal, machine-readable description of the concepts in a domain and the relationships among them. The definition most people cite comes from Tom Gruber, whose 1993 paper described an ontology as “a specification of a conceptualization” and argued that it needs axioms that constrain how its terms can be read, not only a hierarchy. Natalya Noy and Deborah McGuinness, in Stanford’s Ontology Development 101 guide, call it a formal explicit description of concepts in a domain of discourse.
Most ontologies contain four building blocks:
- Classes, the kinds of things that exist, such as Customer, Account, or Transaction.
- Properties and relations, such as “Customer holds Account.”
- Individuals, the actual instances of each class.
- Axioms and constraints, such as “every Account has exactly one owner.”
The standard language is OWL 2, a W3C Recommendation whose second edition was published on 11 December 2012. OWL gives classes, properties, and individuals formal meaning, so reasoners can check consistency and infer facts nobody wrote down. Noy and McGuinness note that an ontology plus its instances is a knowledge base, which is the idea behind today’s knowledge graphs.
Two properties separate an ontology from a data model. First, it is reusable. John Mylopoulos’s University of Toronto lecture notes contrast a university ontology that defines “student” for everyone with a conceptual schema that means only this year’s students in one system. Second, it describes the domain, not a database. The Gene Ontology and SNOMED CT, whose International Edition holds more than 360,000 concepts, exist independently of any one hospital’s tables.
The AI Context Stack
A brief on the layers AI needs to understand enterprise data, from metadata and lineage to semantic definitions and ontology.
Get the AI Context StackWhat is a semantic model in a data stack?
In analytics, a semantic model is a layer that turns physical tables into business measures and dimensions, so people and tools can query “revenue by region” without knowing the joins underneath. Its job is computational consistency: the same metric returns the same number in every tool.
The dbt documentation describes a semantic model as the metadata that tells MetricFlow how one dbt model takes part in metric calculations. It has three parts:
- Entities, the join keys (primary, foreign, unique, or natural).
- Dimensions, the attributes you group or slice by, either time or categorical.
- Simple metrics (formerly measures), the aggregations over columns.
The payoff, per the dbt Semantic Layer overview, is that a metric definition changed in dbt is refreshed everywhere it is used. Power BI semantic models do the same job with measures, hierarchies, and table relationships for reports and dashboards.
The warehouse and lakehouse platforms have each shipped a version. Snowflake offers semantic views, schema-level objects that hold business definitions for Cortex Analyst. Databricks offers metric views, which implement business semantics in Unity Catalog.
Look at what is missing. Its entities are join keys, not concepts with definitions. It does not say what a customer is, which rules a customer must satisfy, or how a customer relates to a contract outside the joins a report needs. It answers “how do we calculate this?” and stops there. A semantic model is built for one set of tables and one class of question, which is exactly the application-specific scope John Mylopoulos’s Toronto lecture notes say an ontology avoids.
How do an ontology and a semantic model differ?
They model different things for different consumers. An IT consultancy’s overview sums it up as measurement for the semantic model and meaning for the ontology. One metadata platform’s comparison frames it as computational consistency against semantic consistency.
| Dimension | Semantic model (BI sense) | Ontology |
|---|---|---|
| Question it answers | How do we calculate this metric? | What are the things in our business, and how do they relate? |
| Core units | Measures, dimensions, join paths | Classes, properties, relationships, axioms |
| Scope | One set of tables or one domain | The domain itself, reusable across systems |
| Main consumers | Analysts, BI tools, dashboards | Applications, integration layers, AI agents |
| Typical owner | Analytics engineers | Knowledge engineers, data architects |
| Standards and tools | dbt MetricFlow, LookML, Power BI, Snowflake semantic views | OWL 2, RDF, SKOS, SHACL, Microsoft Fabric IQ |
| Reasoning | Aggregation only | Inference, consistency checks, classification |
Tooling row sources: MetricFlow, LookML, Snowflake semantic views, Fabric IQ, and the W3C specifications for OWL 2, RDF, SKOS, and SHACL.
A data trust vendor’s guide (September 2026) gives an example. The percentage of deliveries that were late is a semantic model question: one governed calculation. Which orders, suppliers, and customers are affected, and what the business may do about it, is an ontology question, because it needs entities, relationships, and permitted actions.
Each layer fails differently. Without a semantic model, two dashboards report two revenue figures. Without an ontology, the same guide notes, a metric can be reported twice for one company that appears under two identifiers, because nothing defines how customers resolve across systems.
Where they overlap: both need a shared vocabulary, both depend on accurate metadata, and both go stale when source systems change.
Do you need an ontology, a semantic model, or both?
Start from what is failing, not from the vocabulary. A data trust vendor’s “Failure First Test” asks four questions:
- Is the problem calculation or meaning? If teams disagree on how a metric is computed, build a semantic model. If systems disagree about what a record represents, build an ontology.
- What consumes the answer? Dashboards point to a semantic model. Operational apps and cross-domain agents point to an ontology.
- Do relationships matter as much as attributes? Grouping and summing is semantic model work. Walking paths between entities is ontology work.
- Does anything have to act? Read-only reporting needs a semantic model. Workflows that change records need an ontology.
AI agents are pushing most enterprises toward both. Amrutha Satishkumar, a Microsoft solution engineer writing on The Data Grill (July 2026), walks through a retail case. A semantic model alone reports that flagship revenue fell 12 percent, concentrated in the Northeast. Adding an ontology lets the agent trace products to stores, suppliers, and promotions, and find that a promotion ended while a supplier delay caused stockouts.
The same author warns that an ontology generated from a semantic model is a starting point. Relationships, rules, and actions still have to be added by people who know the business.
Neither layer creates the metadata it runs on. One metadata platform’s comparison describes a schema change during a migration that left a semantic model producing consistently wrong lifetime value, and an ontology built from old documentation that missed new product lines. If lineage is incomplete and definitions have no owners, fix that foundation before buying either layer.
AI Agent Context Readiness Checklist
Check whether your lineage, definitions, and ownership are ready to support a semantic model, an ontology, or both.
Get the ChecklistHow does Atlan connect ontologies and semantic models?
Atlan starts from the shared vocabulary that both layers depend on. Its semantic layer models four things: entities with primary keys, named relationships between entities (such as a refund reversing a payment), dimensions, and measures. Because relationships are first-class, the model carries some of the meaning an ontology holds, and a measure defined once updates everywhere it is used.
Workday is the clearest example. According to the Re:Govern 2025 session page, Workday’s people already shared one language through 1,000 glossary terms and 6 million cataloged assets. AI changed the problem: machines could not read that business logic the way humans could, and guessed instead of asking. Workday’s answer was a semantic layer that teaches machines the meaning behind the data, and governance that teaches AI to say “I don’t know” when context is missing. Joe DosSantos, Workday’s VP of Enterprise Data and Analytics, says on the semantic layer page that Atlan captures Workday’s shared language and serves it to AI through its MCP server.
For a deeper comparison of ontologies against full semantic layers, see ontology vs. semantic layer. If your semantic model lives in Power BI, read Power BI semantic models explained. For how ontologies and context graphs fit together for AI, see Ontologies, Context Graphs, and Semantic Layers.
Atlan in Action: Live Context Layer Demos
See how Atlan models entities, relationships, and measures once and serves that shared language to every BI tool and AI agent.
Watch the Live DemoFAQs about ontologies and semantic models
Is a knowledge graph the same as an ontology?
No. The ontology is the schema and the knowledge graph is the data that fills it. The ontology says Customer holds Account and every Account has one owner; the graph holds the actual customers and accounts as nodes and edges. Noy and McGuinness put it simply: an ontology plus its instances is a knowledge base.
Is a taxonomy an ontology?
A taxonomy is a simpler structure. It arranges concepts in a parent-child hierarchy, often encoded in SKOS. An ontology adds typed relationships beyond “broader” and “narrower,” plus properties and constraints. Heather Hedden treats both as semantic structures, and many ontologies use a taxonomy as their backbone.
Can you generate an ontology from a semantic model?
Partly. Microsoft Fabric IQ can bootstrap an ontology from a Power BI semantic model, carrying over its definitions and terminology. The Data Grill calls the result a starting point: the rules, cross-domain relationships, and permitted actions that make an ontology useful to an agent still need to be modeled by people who know the business.
Is a semantic layer the same as a semantic model?
Close, but not identical. A semantic model usually refers to one modeled unit, such as a Power BI semantic model or a dbt semantic model over one table. A semantic layer is the shared service that holds many of them and serves governed metrics to every tool, as the dbt Semantic Layer does through MetricFlow.
Do AI agents need an ontology?
Agents that only answer metric questions in one domain can work from a semantic model. Agents that reason across domains, follow relationships, or take actions need an ontology, because they must know what each entity is and what they may change. One data trust vendor’s guide recommends building both, with a written boundary, once answers start triggering actions.
Sources
- Taxonomies and Ontologies as Semantic Models, The Accidental Taxonomist (Heather Hedden)
- Thoughts on Power BI datasets being renamed to semantic models, Chris Webb’s BI Blog
- Semantic models, dbt documentation
- The dbt Semantic Layer, dbt documentation
- Ontologies vs semantic models: what they mean for data agents and Power BI, The Data Grill
- Semantic layer vs ontology, r/dataengineering
- Ontology (information science), Wikipedia
- Ontology Development 101, Noy and McGuinness, Stanford University
- Ontologies, lecture notes, John Mylopoulos, University of Toronto
- OWL 2 Web Ontology Language Document Overview, W3C
- RDF 1.1 Concepts and Abstract Syntax, W3C
- SKOS Simple Knowledge Organization System Reference, W3C
- Shapes Constraint Language (SHACL), W3C
- What is SNOMED CT, SNOMED International
- Overview of semantic views, Snowflake documentation
- What is LookML, Looker documentation
- An IT consultancy’s overview of semantic models, ontologies and catalogs
- One metadata platform’s comparison of ontologies and semantic layers
- A data trust vendor’s guide to ontologies and semantic layers
