Magnowlia

Business Ontology for Data & Metrics

Magnowlia uses a four-layer ontology framework to give your data a clear business meaning: business domain, business vocabulary, mapping, and technical layers. Define metrics and dimensions in business terms while staying precisely mapped to your database.

The four-layer architecture

Each layer has a clear responsibility. Business concepts sit at the top, physical tables at the bottom, and the mapping and vocabulary layers bridge between them.

b:

Business Domain

Your actual business entities: customers, orders, products, campaigns. Defined as OWL classes with properties and relationships that mirror how your business thinks about data.

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bv:

Business Vocabulary

Framework types for defining metrics (e.g. Total Revenue, Conversion Rate), time dimensions, and business constants. This is where metric expressions and SQL templates live. View vocabulary →

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m:

Mapping

Links business concepts to the physical database: mapsToTable, mapsToColumn, join conditions. This is what makes every metric traceable to its source. View vocabulary →

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t:

Technical

The physical database schema: tables and columns in your data warehouse (BigQuery, Snowflake, PostgreSQL, Redshift). Imported automatically when you connect a data source.

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What is a business ontology?

Learn what a business ontology is, why it matters for analytics and metrics, and how it differs from a simple data dictionary.

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Benefits of an ontology

Consistent metrics, traceability, and a single source of truth. See how an ontology improves your semantic layer and AI analytics.

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Ontology and semantic layer

How a business ontology underpins a semantic layer and powers Magnowlia's ontology-based metrics and dimensions.

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Ontology examples

Real-world Turtle snippets for e-commerce, SaaS, and marketing. See how to model metrics, dimensions, and relationships.

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Ontology vs data dictionary

Compare scope, relationships, metrics, AI-readability, and traceability. Learn when you need each.

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Ontology vs semantic layer

How a formal OWL/Turtle ontology compares with YAML-based semantic layers like dbt, Cube, and Looker.

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Ontology for AI analytics

Why AI needs structured domain context, and how an ontology gives LLMs the definitions and relationships they need.

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Technical documentation

Vocabularies and sample ontologies in OWL/Turtle for implementers and developers. Use these when building or extending ontologies that integrate with Magnowlia.

Mapping vocabulary

Classes and properties for mapping business concepts to physical database structures (prefix m:).

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Business vocabulary

Framework vocabulary for defining business metrics, time dimensions, and constants (prefix bv:).

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Acme Shop sample ontology

A complete example ontology for an e-commerce platform: technical, mapping, business vocabulary, and business domain layers.

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Example: Acme Shop ontology

See how a real ontology looks. The Acme Shop example models an e-commerce platform with all four layers.

b:Order a owl:Class ;
    rdfs:label "Order" ;
    rdfs:comment "A customer purchase transaction" ;
    m:mapsToTable t:public.orders .

b:TotalRevenue a bv:Metric ;
    rdfs:label "Total Revenue" ;
    bv:metricExpression "SUM(total_amount)" ;
    bv:timeDimension b:OrderDate ;
    bv:metricTable t:public.orders .

The business class b:Order maps to a physical table, and the metric b:TotalRevenue defines exactly how revenue is calculated. View the full Acme Shop ontology →

Frequently asked questions

What is a business ontology?+
A business ontology is a formal model that defines your domain's concepts, metrics, dimensions, and relationships in a way both people and systems can use. Unlike a data dictionary (which describes columns), an ontology connects business meaning to database schema so analytics stay consistent.
How is an ontology different from a data dictionary?+
A data dictionary describes tables and column types. A business ontology adds metrics, dimensions, relationships, alternative labels, and explicit mappings to the database. It gives your data a business vocabulary that AI and BI tools can reason about.
What format does Magnowlia use?+
Magnowlia ontologies use OWL (Web Ontology Language) serialized in Turtle format — both are W3C standards widely used in knowledge engineering. The format is human-readable and version-controllable in Git.
Do I need to know Turtle syntax?+
No. Magnowlia includes an AI assistant (powered by Claude) that helps you build and edit your ontology through natural-language conversation. It generates the Turtle syntax for you, and you can review and apply changes with a single click.
Can I use my existing semantic layer?+
Yes. If you already use dbt, Cube, or another semantic layer tool, you can complement it with a Magnowlia ontology. The ontology adds formal relationships, business context, and AI-readability on top of your existing metric definitions.
What data sources are supported?+
Magnowlia connects to BigQuery, Snowflake, PostgreSQL, and Redshift. When you connect a data source, the technical layer (tables and columns) is imported automatically, giving you a head start on building the ontology.

Built on open standards

Magnowlia ontologies use W3C standards: OWL for classes and relationships, RDFS for labels and descriptions, SKOS for alternative labels, and Turtle as the serialization format.

W3C OWLRDF/TurtleRDFSSKOS

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Business Ontology for Data & Metrics | Magnowlia | Magnowlia