--- title: "What Is an Ontology in AI? | Dawiso" description: "An ontology is a formal model of the concepts, properties, and relationships in a domain that AI can reason over. Learn how it powers knowledge graphs." url: "https://www.dawiso.com/glossary/ontology-in-ai" language: "en" --- June 10, 2026 ontologyontology in AIknowledge graphGraphRAGsemantic layerneuro-symbolic AItaxonomy # What Is an Ontology in AI? An **ontology** is a formal, explicit model of the concepts in a domain, their properties, and - crucially - the relationships between them, expressed in a way that both humans and machines can interpret. Where a glossary defines terms in prose, an ontology defines them *structurally*: it says that a *Customer* *places* an *Order*, that an *Order* *contains* *Products*, and that a *Customer* is a kind of *Party* - encoding not just what each concept is but how it connects and what rules govern it. In AI, an ontology is the shared, machine-readable map of meaning that lets a system *reason* about a domain rather than merely store facts about it. It matters because reasoning requires structure. A large language model trained on the open web has broad general knowledge but no formal model of *your* business's concepts and rules - so it cannot reliably infer that a refunded order shouldn't count toward revenue, or that a "lapsed" customer is a former "active" one. An ontology supplies exactly that structured domain knowledge, and it has become central to reliable enterprise AI: it is the backbone of [knowledge graphs](https://www.dawiso.com/glossary/knowledge-graph), the structure behind [GraphRAG](https://www.dawiso.com/glossary/graph-rag), and a key way to ground LLMs in a domain's real relationships instead of leaving them to guess. TL;DR An **ontology** is a formal, machine-readable model of a domain's *concepts, properties, and relationships* (and the rules among them) - a shared vocabulary *with structure*. It is richer than a **taxonomy** (which only captures hierarchy / is-a relationships): an ontology captures arbitrary typed relationships and supports inference. In AI it provides the structured domain knowledge a model lacks, powering [knowledge graphs](https://www.dawiso.com/glossary/knowledge-graph), [GraphRAG](https://www.dawiso.com/glossary/graph-rag), and the grounding of LLMs in real relationships. It is closely related to a [semantic layer](https://www.dawiso.com/glossary/semantic-layer) but more formal and reasoning-oriented. Practically, the concepts and relationships an ontology defines come from a governed [business glossary](https://www.dawiso.com/glossary/business-glossary) and catalog made relational. ## Ontology Defined The classic definition, from Tom Gruber, is that an ontology is "an explicit specification of a conceptualization." Unpacked: it makes explicit (writes down, formally) a shared understanding (conceptualization) of a domain. An ontology typically specifies **classes** (the types of things - Customer, Order, Product), **properties** (attributes of those things - a Customer has a name and a tier), **relationships** (how classes connect - Customer *places* Order), and often **rules or axioms** (constraints and logic - every Order must have at least one Product). The defining quality is that this is all *machine-interpretable*. Because the concepts and relationships are formally specified, software can traverse them, validate against them, and *infer* new facts from them - for example, deducing that if a Customer places an Order and that Order is in a Region, the Customer is associated with that Region, even if no one stated it directly. That capacity for inference is what separates an ontology from a mere data dictionary. ## Ontology vs Taxonomy Ontologies are often confused with taxonomies, but an ontology is strictly richer: - A **taxonomy** organizes concepts into a hierarchy - parent/child, broader/narrower, "is-a-kind-of." It answers *classification* questions (a Sedan is a kind of Car is a kind of Vehicle). It is a tree. - An **ontology** includes hierarchy *but adds arbitrary typed relationships and rules* - not just "is-a" but "places," "owns," "depends-on," "governs." It answers *relationship and inference* questions. It is a graph, not just a tree. Put simply, a taxonomy tells you what something *is*; an ontology tells you what something is *and how it relates to everything else*. That extra relational richness is precisely what AI reasoning needs. ## Its Role in AI Ontologies have been part of AI since long before the LLM era - they are the foundation of the "symbolic" or knowledge-based tradition, where intelligence comes from explicit, reasoned-over knowledge. Today they play three reinforcing roles in modern, LLM-centric AI: - **Domain grounding.** An ontology gives a general-purpose model the specific concepts, relationships, and rules of *your* domain, so it reasons about your business correctly rather than from generic web knowledge. - **Structure for retrieval.** Ontology-structured knowledge underpins [GraphRAG](https://www.dawiso.com/glossary/graph-rag): instead of retrieving disconnected text chunks, the AI traverses defined relationships to assemble connected, relevant context. - **Consistency & validation.** Because an ontology encodes rules, it can constrain and check outputs - catching answers that violate the domain's logic (e.g. treating a cancelled order as revenue). The broad trend is "neuro-symbolic" AI: combining the fluency of neural models with the rigor of symbolic ontologies, so the system is both articulate and correct. ## Ontologies & Knowledge Graphs Ontologies and [knowledge graphs](https://www.dawiso.com/glossary/knowledge-graph) are tightly linked and often confused. The cleanest way to see the relationship: the **ontology is the schema; the knowledge graph is the data**. The ontology defines the *types* of things and relationships that can exist (the classes Customer, Order; the relationship *places*); the knowledge graph is populated with the actual *instances* (this specific customer placed these specific orders). An ontology without instances is an empty model; a knowledge graph without an ontology is a pile of edges with no agreed meaning. Together they give AI a structured, queryable, reasoning-ready representation of a domain - which is why both are central to grounding enterprise AI. ## How Dawiso Uses It Dawiso brings the ontology idea down to earth for data governance: the concepts and relationships an ontology formalises are exactly what a governed [business glossary](https://www.dawiso.com/glossary/business-glossary) and connected [catalog](https://www.dawiso.com/glossary/data-catalog) capture. Business terms are the concepts; the relationships between terms, and between terms and the physical data that implements them, form the structured model; and AI-assisted enrichment helps build it by proposing those relationships rather than requiring them all to be hand-modelled. The result is a connected, machine-readable map of your domain's meaning - the practical, governed form of an ontology - which the [Context Layer](https://www.dawiso.com/context-layer) serves to AI agents through MCP. So an agent reasoning over your data does so with your domain's concepts and relationships, not just generic knowledge: ontology-grade structure, delivered from the catalog you already govern. ## Conclusion An ontology is the structured backbone of meaning - a formal, machine-readable model of a domain's concepts, relationships, and rules that lets AI *reason* rather than merely recall. It goes beyond a taxonomy's hierarchy to capture how everything relates, and it pairs with knowledge graphs (schema and data) to give AI a connected, inference-capable view of a domain. In an era where fluent models still lack grounded domain knowledge, ontologies supply exactly what's missing. You do not need a research project to get the benefit: capture your concepts and their relationships in a governed glossary and catalog, and you have the working ontology your AI needs to understand your business. [See it in action](https://www.dawiso.com/product/ai-powered-features) AI-Powered Features AI-assisted documentation, intelligent search, and automated categorization help you work smarter. ### Table of contents Ontology DefinedOntology vs TaxonomyIts Role in AIOntologies & Knowledge GraphsHow Dawiso Uses It ## Related Terms ### [Ontology vs Semantic Layer](https://www.dawiso.com/glossary/ontology-vs-semantic-layer) Ontology vs semantic layer - an ontology models concepts and relationships for reasoning; a semantic layer defines metrics for consistent analytics. Learn the difference. Read more → ### [What Is a Context Graph?](https://www.dawiso.com/glossary/context-graph) A context graph connects business concepts, data, processes, and people into one relationship model that grounds AI reasoning. Learn how it differs from a knowledge graph. Read more → ### [What Is GraphRAG?](https://www.dawiso.com/glossary/graph-rag) GraphRAG combines knowledge graphs with retrieval-augmented generation to give LLMs richer, relationship-aware context. Learn how it reduces hallucinations and enables complex enterprise Q&A. Read more → ### [What Is Databricks Genie Ontology?](https://www.dawiso.com/glossary/databricks-genie-ontology) Databricks Genie Ontology is a context layer that grounds Genie in business meaning. Learn how Unity Catalog feeds it and where a cross-platform layer reaches. Read more → ### [What Is a Knowledge Graph?](https://www.dawiso.com/glossary/knowledge-graph) A knowledge graph connects data via entities and typed relationships so AI can reason with context. How graphs power RAG, semantic search, and governance. Read more → ### [What Is a Metadata Knowledge Graph?](https://www.dawiso.com/glossary/metadata-knowledge-graph) A metadata knowledge graph models data assets, terms, owners, and policies as connected nodes and relationships - powering discovery, impact analysis, and AI context. Learn how. Read more →