An ontology defines the formal vocabulary of a domain: the classes of things that exist, the properties that describe them, and the relationships that connect them. In data management and AI, ontologies make business meaning machine-readable so that AI agents, governance policies, and analytics tools operate from a single, consistent understanding of enterprise concepts.

Core Components:
1. Structure: Classes, properties, and relationships in machine-readable formats like RDF and OWL
2. Reasoning: Enables inference — AI derives new facts from stated facts using formal logic
3. Governance: Defines what business terms mean, how they relate, and who owns them
4. Interoperability: Shared vocabulary allowing different AI systems and platforms to communicate


Attribute	Detail
Definition	A formal model of concepts, properties, and relationships within a domain
Origin	Philosophy (Aristotle); formalized for computer science by Tom Gruber (1993)
Key standards	RDF (Resource Description Framework), OWL (Web Ontology Language), both W3C
Primary use in AI	Grounding AI agents with structured business context for accurate outputs
Related concepts	Taxonomy (hierarchical classification), Schema (data structure), Semantic layer (query abstraction), Knowledge graph (data instantiation)
Enterprise value	Reduces AI hallucination, enables cross-platform governance, accelerates AI agent deployment
Governance role	Defines what business terms mean, how they relate, and who owns them
Modern implementation	Active metadata platforms bootstrap ontology incrementally, not through multi-year modeling
When did ontology become important?
The idea is not new. Philosophers have debated ontology since Aristotle. Computer scientists formalized it in the 1990s. What changed is the stakes. 88% of organizations now use AI in at least one business function, but fewer than 40% have scaled beyond pilot. That gap between pilot and production is, in large part, a context gap. Ontology fills it.

Ontology formalizes “what things are” and “how they relate” in a domain
Taxonomies classify; ontologies define meaning, constraints, and reasoning rules
In AI, ontology is the structural layer between raw metadata and intelligent action
Enterprise ontology covers business terms, data assets, policies, and their connections
Modern ontologies are living, evolving structures, not static academic models
Taxonomies classify things. Ontologies define meaning, relationships, and reasoning rules.

Taxonomies classify things. Ontologies define meaning, relationships, and reasoning rules. Image by Atlan.

How ontology works in AI and data management
Ontology works by defining classes (categories of entities), properties (attributes and data types), and relationships (how entities connect) in machine-readable formats like RDF and OWL. AI systems use this formal structure to perform inference, resolve ambiguity, and navigate enterprise data without requiring manual mapping for every new use case.

What are classes, properties, and relationships in ontology?

Every ontology rests on three building blocks. Classes are the categories: “Revenue,” “Customer,” “Data Pipeline.” Properties describe each class: Revenue has a currency, a fiscal period, a flag for whether it includes returns. Relationships connect classes to each other: Revenue belongs to Financial Metrics, Customer owns Account, Account generates Revenue.

This is not abstract. Consider a financial reporting team and a sales ops team that both use the word “revenue.” Finance means GAAP-recognized revenue. Sales means bookings. Without an ontology that defines both as distinct classes with explicit properties and constraints, an AI agent has no way to know which one a query refers to. It picks whichever definition it encounters first. Gartner predicts 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025. Each of those agents needs this kind of formal semantic structure to function. A business glossary defines the human-readable layer; ontology makes that layer machine-readable.

How do RDF and OWL make ontology machine-readable?

RDF (Resource Description Framework) represents facts as triples: subject, predicate, object. “Revenue belongs_to Financial_Metrics” is one triple. Stack enough triples and you have a graph of your domain. The W3C published RDF 1.1 as a formal recommendation in 2014, and it remains the foundational standard for linked data and the Semantic Web.

OWL (Web Ontology Language) adds expressive logic on top of RDF. Class hierarchies, cardinality constraints (“a Customer must have exactly one primary Account”), equivalence declarations (“Bookings_Revenue is equivalent to Sales_Revenue”). SPARQL is the query language for traversing these structures. These are W3C standards, which means ontology built on RDF and OWL is interoperable across tools and platforms. That interoperability is what separates open ontology from proprietary approaches that lock semantic models into a single vendor. A semantic layer maps business terms to physical data for querying; ontology defines what those terms mean at a deeper structural level. dbt semantic layer integration bridges the two in practice.

How does ontology enable inference and reasoning?

Ontology enables machines to derive new facts from stated facts. If the ontology says “Revenue includes Subscription_Revenue” and “Subscription_Revenue excludes One_Time_Fees,” an AI agent infers that Revenue excludes One_Time_Fees. No one programmed that rule explicitly. The ontology’s formal logic made it derivable.

This is what separates ontology-grounded retrieval from pure vector search. Embedding-based retrieval finds documents that are statistically similar to a query. Ontology-grounded retrieval finds answers that are logically consistent with defined business meaning. RAG systems with structured knowledge context reduce AI hallucinations by over 40% compared to traditional approaches, according to the MEGA-RAG study published in PubMed Central. Enterprise RAG deployments frequently underperform when they lack semantic structure, producing answers that are statistically plausible but logically inconsistent with business definitions. A context graph unifies semantic meaning with operational lineage, giving AI agents both the “what” and the “where” of enterprise data.

Comparison: ontology vs. taxonomy vs. schema vs. semantic layer vs. knowledge graph

Concept	What it defines	Structure	Machine-readable?	Supports reasoning?	Example
Ontology	Formal model of concepts, properties, and relationships in a domain	Graph (classes + properties + axioms)	Yes (RDF/OWL)	Yes, inference and logical constraints	“Revenue is a Financial Metric that includes Subscription Revenue and excludes One-Time Fees”
Taxonomy	Hierarchical classification of terms	Tree (parent-child)	Partially (SKOS)	No, classification only	“Financial Metrics > Revenue > Subscription Revenue”
Schema	Structure and data types of a dataset	Tabular (columns + types + constraints)	Yes (DDL, JSON Schema)	No, structural validation only	“revenue_amount: DECIMAL(18,2), NOT NULL”
Semantic layer	Abstraction mapping business terms to physical data for querying	Mapping layer (metrics + dimensions)	Yes (dbt, LookML)	No, query translation only	“Revenue = SUM(order_amount) WHERE status = ‘completed’”
Knowledge graph	Data instances organized by an ontology’s structure	Graph (nodes + edges with real data)	Yes (RDF triples, property graphs)	Yes, if backed by an ontology	“Acme Corp [has_revenue] $4.2M [in_period] Q1-2026”
Why enterprise AI needs ontology
Enterprise AI needs ontology because AI agents cannot reliably interpret data without a formal model of business meaning. Ontology grounds AI outputs in defined concepts and relationships, reduces hallucination by providing structured context for retrieval, and enables governance to scale across platforms without manual re-mapping.

How do ontologies ground AI agents in business meaning?

AI agents retrieve data. They do not understand it. When an agent queries “revenue,” it pulls whatever matches from the nearest data source. If your finance team defines revenue as GAAP-recognized net revenue, your sales team defines it as bookings, and your product team defines it as MRR, the agent picks whichever definition it hits first.

A separate Deloitte survey found that 62% of data leaders cite “lack of semantic consistency” as the primary barrier to scaling AI across business units. An ontology prevents this by giving agents an explicit map of what “revenue” means in context — what it includes, what it excludes, and which team’s definition applies. Active metadata enriches every data asset with governed meaning, turning static documentation into live signals that agents query at inference time.

How does ontology reduce AI hallucination?

Start with the cost: poor data quality costs the average organization $12.9 million annually, according to Gartner. Hallucination compounds that by generating confident but incorrect outputs that propagate through downstream decisions.

RAG (Retrieval-Augmented Generation) helps, but RAG alone retrieves documents — it does not understand concepts. An embedding search for “quarterly revenue” returns statistically similar text. An ontology-grounded search returns the specific definition of quarterly revenue, its component metrics, its exclusions, and its relationship to annual revenue. The difference is the gap between plausible and correct. A data catalog for AI provides the operational layer for ontology-grounded retrieval.

How does ontology enable cross-platform governance?

Here is the scenario most governance teams recognize: you define a masking policy for PII fields in Snowflake, then redefine it in Databricks, then again in your BI layer. Each platform has its own naming conventions, its own metadata structure, its own interpretation of what “PII” means. Divergence is inevitable.

Ontology breaks the cycle. When “PII_Field” is defined as a class with a “requires_masking” constraint, that rule propagates across every platform that reads the ontology. Define once, enforce everywhere. By 2027, organizations adopting active metadata practices will increase by more than 75% across data, analytics, and AI, according to Gartner’s Market Guide for Active Metadata Management. See data governance platforms that operationalize this approach.

The Data Stack Is Being Rebuilt for AI. Here's What's Coming.
What happens when AI isn't just a feature on top of data, but a consumer of it? Based on insights from 550+ data leaders, Prukalpa Sankar (Co-Founder & Co-CEO, Atlan) shares seven predictions—from the rise of data agents to why the context layer becomes the new infrastructure.


How to build an enterprise ontology
Building an enterprise ontology starts with a focused domain. Identify 3-5 high-value business domains, define core classes and relationships in each, connect them to existing metadata in your data catalog, and iterate. Active metadata platforms bootstrap ontology incrementally from existing lineage, glossary terms, and usage patterns.

Prerequisites:

Data catalog with asset inventory across key platforms
Business glossary with at least core terms defined
Column-level lineage mapped for priority data assets
Executive sponsor (CDO or VP Data) who owns semantic standards
Cross-functional stakeholders identified (data engineering, governance, analytics, AI/ML)
Seven steps to your first production ontology:

Select 3-5 priority domains. Choose domains where AI agents are active or planned: financial metrics, customer entities, product hierarchies. Start narrow. A focused ontology that covers one domain well is worth more than a sprawling model that covers everything poorly.

Audit existing semantic assets. Inventory business glossary terms, dbt semantic models, schema documentation, and tribal knowledge. Most organizations already have 40-60% of an ontology scattered across tools. MuleSoft’s 2025 Connectivity Benchmark found that only 28% of enterprise applications are integrated, despite organizations averaging 897 apps — semantic definitions fragment across these disconnected systems. The work is consolidation, not creation from scratch.

Define classes and properties. For each domain, identify the core classes (entities), their properties (attributes), and data types. Use existing glossary terms as the starting vocabulary. “Revenue” becomes a class. “Currency,” “fiscal_period,” and “includes_returns” become its properties.

Map relationships and constraints. Define how classes connect: “Revenue includes Subscription_Revenue,” “Customer owns Account,” “PII_Field requires Masking_Policy.” Add cardinality constraints where business logic demands them. Teams that define explicit cardinality constraints in their ontology reduce downstream data quality incidents by 34%, according to Gartner.

Connect to live metadata. Link ontology classes to physical data assets in your catalog. Each class maps to tables, columns, dashboards, and pipelines through column-level lineage. This is what separates a useful ontology from a theoretical model.

Validate with AI agent use cases. Test ontology coverage by running target AI agent queries against it. Where the agent cannot resolve a concept, the ontology has a gap. Fill it.

Establish governance and iteration cadence. Assign domain owners, set review cycles (monthly minimum), and automate drift detection. Ontology is never “done.” Business definitions change. New data assets appear. The ontology must keep pace.

Build your first production ontology in seven clear, repeatable steps.

Build your first production ontology in seven clear, repeatable steps. Image by Atlan.

Organizations with active metadata management reduce time to deliver new data assets by up to 70%, according to Gartner. An enterprise context layer operationalizes ontology so it stays connected to the living data stack.

Common pitfalls:

Boil-the-ocean scope: trying to model the entire enterprise in one initiative instead of starting with 3-5 domains
Treating ontology as a one-time project instead of a living, governed structure with ownership and review cycles
Building ontology in isolation from the data catalog and lineage, which creates a disconnected model nobody uses
Over-engineering with academic OWL constructs when simpler property-graph models serve the same purpose for your use cases
How to evaluate ontology approaches
Evaluating ontology approaches requires assessing five criteria: openness of architecture, integration with existing data stack tools, support for incremental adoption, governance lifecycle capabilities, and AI agent compatibility.

Criterion	What to assess	Red flag	Green flag
Architecture openness	Does the ontology layer work across your full data stack or only within one vendor?	Proprietary ontology locked to a single platform	Open APIs, MCP support, multi-platform connectors
Incremental adoption	Can you start with 3-5 domains and expand, or does it require full upfront modeling?	Requires complete enterprise model before delivering value	Domain-by-domain rollout with immediate value
Governance lifecycle	Does ontology evolve as business context changes, with versioning and ownership?	Static model with no change management	Automated drift detection, domain ownership, review cadence
AI agent compatibility	Can AI agents query the ontology dynamically for context during inference?	Ontology is a reference document, not a queryable layer	Real-time API or MCP access for AI agents
Stack integration depth	Does it connect to your existing catalog, lineage, glossary, and semantic layer?	Standalone ontology tool requiring separate data entry	Native integration with catalog, lineage, dbt, BI tools
Total cost of adoption	What is the time-to-value: weeks, months, or years?	12+ month implementation before first use case	Weeks to first domain, months to cross-domain coverage
More than 80% of enterprises will have used generative AI APIs or deployed generative AI-enabled applications by 2026, according to Gartner. The ontology approach you choose now determines whether those AI deployments produce correct answers or plausible ones.

Questions to ask vendors:

How does your ontology layer integrate with our existing data catalog and lineage?
Can AI agents query ontology classes and relationships at inference time via API or MCP?
What does the governance model look like? Who owns ontology domains, and how are changes reviewed?
Can we start with a single domain and expand, or is full enterprise modeling required upfront?
How does your ontology handle evolution when business definitions change?
Does your ontology interoperate with open standards (RDF, OWL, property graphs) or is it proprietary?
What is the typical time-to-value for a first production ontology domain?
See how a context layer for Snowflake shows open ontology working in a real production environment.

Adaptive Data Governance for the AI Era
Comprehensive resource on adaptive data governance strategies for the AI era, covering modern approaches to unifying context across data and AI systems, enabling collaboration, and activating metadata for both human and AI-powered experiences.

Enter your email

How Atlan approaches ontology
Atlan operationalizes ontology through its metadata lakehouse and 4-graph architecture: a data graph, governance graph, knowledge graph, and active ontology graph that evolve as the data stack and business context change. AI agents access this living ontology through Atlan’s MCP server, grounding outputs in governed, structured context without requiring static upfront modeling.

Most organizations have semantic meaning scattered across glossaries, dbt models, schema documentation, and tribal knowledge. AI agents cannot access this fragmented context at inference time. Closed-architecture ontology platforms require migrating to a single vendor stack, which means ripping out existing tools before seeing any value.

Atlan takes a different approach. The active ontology is not a standalone modeling tool. It is a living graph within a metadata lakehouse that connects to 100+ data stack integrations. The four graphs work together: a data graph maps assets, schemas, and lineage across every platform. A governance graph tracks policies, ownership, quality rules, and compliance. A knowledge graph stores business terms, classifications, and semantic relationships. The active ontology graph maintains formal classes, properties, constraints, and reasoning rules that evolve automatically as the underlying data changes.

AI agents access this ontology through the MCP server (Model Context Protocol), which makes enterprise ontology queryable by any AI system. Every AI application in your stack can pull structured business context at inference time. Organizations bootstrap ontology from existing catalog metadata, glossary terms, and lineage. No multi-year modeling project. Domain-by-domain rollout with AI agents grounding outputs in governed context from day one.

Atlan is recognized as a Leader in the Gartner Magic Quadrant for Metadata Management Solutions 2025, a Leader in the Gartner Magic Quadrant for Data and Analytics Governance 2026, and a Leader in the Forrester Wave Q3 2024. Explore Atlan’s modern data catalog to see how it works.

See how Atlan operationalizes ontology for your AI agents.

FAQs about ontology
What is ontology in artificial intelligence and why does it matter?

Ontology in AI is a formal model defining concepts, properties, and relationships so machines interpret data accurately. AI agents need structured context to ground outputs in business meaning. Without ontology, AI treats every query as a new problem with no shared understanding of what terms mean.

What is the difference between ontology and taxonomy in data management?

A taxonomy organizes terms into parent-child hierarchies. An ontology goes further: it defines properties, constraints, and logical relationships between concepts, enabling machine reasoning. A taxonomy tells you “Revenue” is a “Financial Metric.” An ontology tells you what Revenue includes, excludes, and how it connects to other entities.

How do ontologies help AI agents understand enterprise data?

Ontologies give AI agents a formal map of business meaning. When an agent queries “revenue,” the ontology specifies which definition applies, what it includes or excludes, and how it relates to concepts like bookings or ARR. This prevents the agent from hallucinating or retrieving the wrong definition.

What is the difference between an ontology and a semantic layer?

A semantic layer maps business terms to physical data so analysts query metrics without writing SQL. An ontology defines formal meaning, relationships, and constraints between concepts at a deeper level. The semantic layer answers “where is this metric?” The ontology answers “what does this metric mean and how does it relate to other concepts?”

How do you build an enterprise ontology without a multi-year project?

Start with 3-5 priority domains where AI agents are active. Audit semantic assets in your data catalog, glossary, and schema documentation. Define core classes and relationships per domain. Connect to live metadata through lineage. Iterate monthly. Active metadata platforms bootstrap ontology incrementally from existing assets.

What role does ontology play in data governance?

Ontology provides governance with a formal semantic backbone. Policies reference ontology classes to define what terms mean, who owns them, and what rules apply. When ontology defines “PII_Field” with a “requires_masking” constraint, that rule propagates automatically across every platform that reads the ontology.

Is ontology the same as a knowledge graph?

Ontology and knowledge graph are related but distinct. An ontology defines formal structure: classes, properties, relationships, and constraints. A knowledge graph instantiates that structure with real data. The ontology says “Customer is a class with properties name, industry, and account_tier.” The knowledge graph says “Acme Corp is a Customer in Manufacturing with Enterprise tier.”

How does ontology reduce AI hallucination in enterprise applications?

Ontology reduces hallucination by providing a verified, structured layer of business meaning that agents consult before generating outputs. Instead of relying on pattern matching over unstructured documents, the agent queries the ontology to confirm what concepts mean, how they connect, and what constraints apply.

Why ontology is the missing layer between metadata and AI action
Ontology is the formal semantic layer that transforms disconnected metadata into a machine-readable model AI agents can reason over. Organizations that operationalize ontology through active metadata platforms reduce hallucination, scale governance across platforms, and deploy AI agents faster.

The pattern is consistent across the data in this guide. 88% of organizations use AI, but fewer than 40% have scaled past pilot. RAG systems with structured knowledge context reduce AI hallucinations by over 40% compared to traditional approaches, according to the MEGA-RAG study published in PubMed Central. The gap between pilot and production is a context gap. Ontology fills it by giving AI agents a formal model of what business terms mean, how they connect, and what constraints apply.

The key is starting incrementally. Pick 3-5 high-value domains. Audit existing semantic assets. Define classes, properties, and relationships. Connect them to live metadata through column-level lineage. Validate against real AI agent queries. Iterate monthly.

Evaluate any ontology approach on openness, incremental adoption, governance lifecycle, and AI agent compatibility. The right approach connects to your existing data catalog and lineage rather than replacing them. Active metadata platforms make ontology operational without the multi-year academic modeling project that stalled previous generations.

Your AI agents are already answering questions about your data. The question is whether those answers are grounded in verified meaning or statistical approximation.
