An ontology defines the concepts, types, and relationships that matter in a domain. It gives knowledge a shared vocabulary so systems can distinguish, for example, an account owner from an account approver.

Is an ontology the same as a knowledge graph?

No. An ontology describes the meaning of types and relationships; a knowledge graph contains assertions about particular things. A graph can use an ontology to make those assertions more consistent and easier to query.

Is an ontology a database schema?

They have similarities, but they serve different purposes. A database schema organizes stored data; an ontology describes the meaning of concepts and relationships in a domain. Validation rules can add requirements that an ontology alone does not enforce.

Do ontologies eliminate hallucinations?

No. An ontology can guide extraction and retrieval, but sources may be wrong, extracted facts may be incomplete, and an LLM can still make mistakes. Source provenance, validation, evaluation, and review remain necessary.

What are RDF, OWL, and SHACL?

RDF represents statements as linked facts; OWL describes classes, properties, and logical relationships; SHACL expresses conditions for validating RDF data. They play complementary roles rather than being interchangeable.

In TrustGraph, OntologyRAG uses an imported ontology to guide extraction of entities and relationships from documents. At query time, semantic similarity finds graph entry points, graph traversal gathers related knowledge, and the resulting subgraph supports an answer.

How is TrustGraph different from a Cypher-based graph system?

Cypher-based systems can retrieve graph patterns effectively, but teams generally must model their domain and build query generation, retrieval, and provenance workflows for their use case. TrustGraph combines ontology-guided extraction with native graph retrieval and source-linked explainability. Neo4j can also use RDF, OWL, and SHACL through extensions, so this is not a claim that Cypher systems cannot support semantics.

Do I need a perfect ontology before I start?

No. Start with the concepts and relationships needed for real questions, test the extracted facts and retrieved answers, then refine the ontology. The hard part is keeping knowledge accurate and useful across sources, versions, and queries—not writing the first set of classes.

What is an Ontology?

An ontology is a description of the concepts and relationships that matter in a domain. If one team says a person owns an account and another says they approve changes to it, an ontology can give those roles distinct names and definitions. That distinction matters when an agent must answer a question—or decide whether a requested change is permitted.

An ontology is not the answer to that question. It is the shared language that makes the answer possible to assemble and check.

The basics: meaning and facts

Suppose a company needs to answer: “Who can approve a change to this supplier's payment details?” A useful ontology might define Supplier, PaymentAccount, Person, ApprovalRole, and relationships such as hasPaymentAccount and authorizedApprover. The knowledge graph then records particular suppliers, people, accounts, and approval assignments using those terms.

That separates two jobs:

Ontology: Defines what an authorized approver is and how that role relates to a supplier or account.
Knowledge graph: Records that a particular person holds that role for a particular supplier, according to an identified source.
Retrieval: Finds the relevant entities and relationships when someone asks a question.
Validation: Checks specified requirements, such as whether a claimed approval assignment has the required fields.

In an RDF-based system, statements connect a subject, predicate, and object. OWL can describe the classes and properties those statements use. SHACL can express conditions to check against the resulting RDF graph. These technologies complement one another: defining a property in OWL does not, by itself, prove that every extracted statement is complete or true.

An ontology is also not simply a taxonomy. A taxonomy can organize terms into categories; an ontology can additionally express the relationships among them. Nor is it merely a database schema. A schema describes how data is stored, while an ontology makes domain meaning explicit so it can be shared across data sources and applications.

What an ontology cannot do

It cannot repair a false source document, identify every entity correctly, or infer a missing approval from silence. An ontology that says an approver must be a Person does not establish that the person named in an email really has approval authority. And because RDF and OWL commonly operate under an open-world assumption, an absent statement is not automatically a statement of falsity. If an application needs to require a value, it should define an explicit validation rule and a policy for what to do when the value is missing.

It cannot make an LLM infallible either. Ontology-guided extraction still depends on the source material and the extraction process. A model can classify an entity incorrectly or overlook a relationship. When the consequence matters, inspect the extracted knowledge, retain source provenance, validate what can be validated, and test answers against real questions.

Finally, an ontology does not retrieve anything on its own. A perfectly described domain sitting in a file is of little use if an application cannot find the right facts quickly, distinguish current evidence from obsolete evidence, and show why it selected a particular answer.

Easy to write, hard to use

A small ontology can be drafted quickly. Pick a few real questions, name the entities they involve, define the relationships needed to answer them, and write those definitions in OWL. TrustGraph lets you bring an existing OWL ontology, work with one in its ontology tooling, or refine one as you learn from your documents. You do not need to model the entire enterprise before the first useful query.

The difficulty begins when knowledge moves beyond a demonstration:

One person or organization appears under several names, and different entities share a name.
Relevant facts are scattered across documents, spreadsheets, and updates.
A relationship was valid last quarter but has since changed.
An extraction appears plausible but cannot be traced to a reliable source.
The ontology grows too large to give every part of it to an LLM for every document.
A question requires several linked facts, not a single matching sentence or node.

This is the difference between having an ontology and making ontology-enhanced knowledge usable at scale. It requires a repeatable pipeline for selecting relevant concepts, extracting and checking facts, connecting them to sources, retrieving the right relationships, and inspecting the answer path. More classes alone do not solve any of these operational problems.

TrustGraph's OntologyRAG addresses one part of that challenge by retrieving the relevant portion of an ontology for each chunk being processed, rather than placing the whole ontology in the model's context. It then uses the ontology to guide extraction into a graph. At query time, semantic similarity identifies graph entry points, traversal gathers related facts, and reranking helps focus the knowledge returned for answer generation. Extraction has time and token costs; accuracy still needs evaluation against your data and questions.

Cypher vs TrustGraph

Cypher is a powerful language for matching patterns in property graphs. If you know the graph model and the question, a Cypher query can retrieve an exact relationship path. The harder question for an AI application is who constructs and maintains the path from natural-language request to correct, source-backed answer, especially as terminology, data, and access needs change.

Dimension	Cypher-based graph application	TrustGraph with OntologyRAG
Primary query interface	Teams write Cypher patterns or build natural-language-to-Cypher and GraphRAG layers around the database.	Native natural-language graph retrieval finds entry points and explores related knowledge; graph queries remain available for explicit patterns.
Domain model	Often modeled as labels, relationship types, properties, and application conventions; RDF/OWL integration is possible with additional tooling.	An imported OWL ontology guides which domain types and relationships are extracted.
Ingestion from documents	Requires an extraction pipeline and mappings into the graph model.	OntologyRAG retrieves relevant ontology terms per chunk and guides structured extraction into the graph.
Retrieval from a question	Query templates or generated Cypher must identify the right nodes, edge types, traversals, and filters.	Semantic similarity identifies candidate entities; traversal and relevance scoring assemble a useful subgraph.
Standards and validation	A property graph can be extended with RDF/OWL import, SHACL validation, and basic inference—for example, with Neo4j's neosemantics extension—but capabilities and deployment options vary.	RDF-based knowledge and OWL ontologies provide standards-oriented semantics; SHACL expresses explicit validation requirements where a compatible validation workflow is configured.
Evidence trail	Source lineage and answer traces must be designed into the application or supplied by additional components.	Extraction provenance and query-time explainability link selected graph knowledge back to source material.

The distinction is not that Cypher cannot retrieve connected facts, or that property graphs cannot use ontologies. They can. Neo4j's neosemantics extension, for example, supports RDF and ontology import, SHACL validation, and basic inferencing on supported self-hosted deployments. The distinction is the amount of application machinery needed to make ontology-guided document extraction, natural-language retrieval, and source-linked explanations work together.

Nor should “supports OWL” be confused with “performs every possible OWL inference,” or “supports SHACL” with “automatically validates every fact.” Specify the OWL features, SHACL shapes, validation points, and failure behavior your application needs, then test them in the deployed configuration. As of September 2026, RDF 1.2 specifications are still on the W3C recommendation track; check implementation support for the particular RDF 1.2 features you intend to use.

From model to useful knowledge

Start with a question your users actually ask. Define only the classes and relationships needed to answer it. Ingest a representative set of source material, inspect what was extracted, and check whether the answer can be traced to the correct evidence. Add SHACL shapes where missing or malformed data would cause a bad decision. Then expand the ontology as new questions expose gaps.

A small ontology connected to well-sourced, retrievable knowledge is more valuable than an elaborate ontology nobody can reliably use. The promise is not magic. It is a shared meaning that survives the journey from a document, through a graph, to an answer an agent can explain.