Ontology, knowledge graphs, and RAG: differences and how they work together
Compare ontology models, knowledge graph connections, and RAG retrieval. Explore how they work together, how they relate to digital twins, and what to evaluate separately.
Ontology, knowledge graphs, and RAG are not three names for the same technology. An ontology defines concepts and relationship meanings. A knowledge graph represents actual objects and links. Retrieval-augmented generation retrieves material to support a generated answer. A workflow may use these approaches together.
These roles extend beyond document search. For equipment and tasks, products and orders, or teams and activities, you can distinguish the shared model from actual records and the capabilities that use them.
Compare their roles
| Approach | Main role | Customer, contract, and order example |
|---|---|---|
| Ontology | Define concepts, relationships, and interpretation | Specify customer and account identities, and how projects relate to orders |
| Knowledge graph | Represent actual objects and connections | Link Customer A, account C-104, a contract, project, and order |
| RAG | Use retrieved evidence for answer generation | Retrieve applicable contract passages and order records |
W3C OWL 2 describes formal ontology representation. The original RAG paper examines combining retrieval with a generation model. Treat these as different modeling and retrieval responsibilities rather than interchangeable product categories.
A document and ERP example
Suppose a fictional sensor-parts order belongs to Customer A's equipment-upgrade project. The question is: “Which project does this order belong to, and which contract terms apply?”
First, define how a customer in a document matches an ERP account. Use a reliable identifier or approved mapping, rather than joining on names alone. Specify the fields that link orders and projects.
Next, represent the connections between actual records. Trace the order to its project and contract, preserving each connection's evidence and effective date. A knowledge graph is one way to manage this structure.
Finally, retrieve original passages from the related contract and relevant order records. The applicable conditions and exceptions still need to come from the actual terms. Verifying a connection and interpreting a clause correctly are separate checks.
Does GraphRAG automatically complete an ontology?
No. Microsoft GraphRAG local search uses graph entities, relationships, and source text as context for a question. Graph-based retrieval does not establish that every business definition has been agreed.
Domain review is still needed to determine whether Customer A matches account C-104, which contract version applies, and who reviews the result. Extracted relationships also require verification against their sources.
Ontology modeling does not always include a formal reasoning engine. Formal inference, graph traversal, and model-generated answers are different operations. Distinguish them in both design and evaluation.
Where do digital twins fit?
A digital twin represents a real object or operation through data and models that reflect changes in reality. NIST's digital twin work highlights connections between data and models, validation, and interoperability.
An ontology can define shared meanings and relationships within a twin. A knowledge graph is one way to represent connected information; RAG can retrieve supporting documents. Combining them does not complete real-time synchronization or simulation. Update frequency, time and state representation, analytical models, and validation need separate design around the intended use.
Which approach should you evaluate first?
For questions about a specific document or source citation, evaluate document retrieval and RAG quality first. If questions repeatedly traverse objects, such as orders and their related contracts, consider relationship modeling and graph exploration alongside retrieval.
To understand work related to an asset's changing status, or orders affected by supply changes, model states, relationships, and update times together. The intended result determines whether the next step is retrieval, analysis, simulation, or another form of use.
If teams interpret terms or statuses differently, agree on their meanings before changing the retrieval method. Source permissions, versions, and evidence remain necessary in each case.
What should an adoption evaluation check?
Evaluate realistic business questions against actual records and expected results. Check correct identity mapping, applicable contract versions, source-grounded answers, and permission boundaries separately.
Include missing connections and absent evidence. A useful system should identify what requires further review rather than fill an unverified relationship with a plausible answer. Also include examples where two similar names belong to different accounts, and where a changed project owner invalidates an earlier assumption.
An overall answer score can hide the source of an error. Record whether it came from an incorrect mapping, an outdated record, missing retrieval, or an unsupported interpretation. That distinction helps determine whether the next improvement belongs in modeling, data maintenance, or answer generation.
Read the enterprise data ontology introduction and adoption checklist. Wissly Ontology Beta explains how to discuss applicability around your data and business questions.