Ontology RAG vs
Traditional RAG
Traditional RAG retrieves text chunks by vector similarity. Ontology RAG retrieves connected entities and relationships from your knowledge graph — so agents reason across multiple hops and cite the exact facts behind every answer.
Meaning, Not Just Similarity
Ontology RAG retrieves connected entities and relationships from the knowledge graph; traditional RAG retrieves text chunks that merely look similar to the query.
Multi-Hop Reasoning
Traversing relationships lets agents answer questions that span multiple entities — something chunk-based RAG cannot follow.
Cited & Low-Hallucination
Every answer traces to defined concepts and their source data, instead of an opaque vector match — so answers stay explainable and checkable.
Ontology RAG is retrieval-augmented generation grounded in an ontology and knowledge graph rather than a flat vector store. Where traditional (naive) RAG splits documents into chunks and retrieves the ones most similar to a query, ontology RAG retrieves the connected entities, relationships, and definitions a question actually needs — enabling multi-hop reasoning, consistent business meaning, and answers traceable to defined concepts, with far lower hallucination.
Traditional RAG was a breakthrough: retrieve relevant text, then let the model answer from it. But chunk-and-embed retrieval has limits — it matches surface similarity, not meaning, so it misses connected context, cannot follow relationships across documents, and grounds answers in loose snippets. Ontology RAG, the approach behind Scrydon's Enterprise RAG, retrieves over your knowledge graph instead: agents pull the right entities and the relationships between them, reason across multiple hops, and trace every answer back to a defined concept and its source. This page explains how the two differ and when ontology-grounded retrieval matters.
Ontology RAG vs Traditional RAG in the Scrydon platform
One integrated, sovereign architecture. Here is where Ontology RAG vs Traditional RAG sits — highlighted against the full stack it works with.
The AI OS (Agentic OS) for Humans & AI Agents to enable your processes
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Link your processes, knowledge & data to ontologies.
Unified storage, structured compute, and secure multi-modal data processing.
Autonomous operatives with specialised skills executing tasks across systems.
Sovereign pipelines, federated APIs, and seamless connector meshes.
Secure domain federation, trusted data sharing, and cross-boundary intelligence.
Ontology RAG vs Traditional RAG in depth
Human + AI Orchestration
The AI OS (Agentic OS) for Humans & AI Agents to enable your processes
The Human + AI Orchestrator is the operational runtime at the heart of the AI OS — also called the Agentic OS — scheduling, routing, and governing every task across your enterprise, whether executed by an AI agent, an existing system, or a human.
Most organisations have broken processes: encoded in siloed systems or locked in people's heads. The AI OS makes them visible and executable. It captures intent, synthesises context, acts — then feeds every result back into the ontology so the next run is smarter. All of it inside your perimeter.
Cognitive Enterprise
Link your processes, knowledge & data to ontologies.
Most organisations have data they can't use — not because it doesn't exist, but because nothing connects it. The Cognitive Enterprise layer is the defining intelligence of the AI OS: a living, queryable semantic model of your organisation's entities, processes, and rules. It is the single source of truth that allows every agent, analyst, and workflow to reason about your business with a consistent understanding.
Without it, AI agents reason on noise. With it, they reason on the business.
- Entity graph: Model customers, accounts, orders, products, and any domain concept — then connect them with typed, traversable relationships.
- Process integration: Link real-world workflows to ontology entities so agents understand how data flows through your business.
- Continuous enrichment: Agents automatically enrich ontology nodes with fresh data from the lakehouse, keeping the model current without manual effort.
Chunk similarity vs connected meaning
Traditional RAG treats your knowledge as a bag of text: documents are chopped into chunks, embedded as vectors, and the chunks closest to a query are returned. It works for simple lookups, but it retrieves by surface similarity, not meaning — so it misses connected context and cannot follow a relationship from one document to another. Ontology RAG retrieves over the knowledge graph instead, pulling the specific entities and relationships a question needs, already linked and defined.
Traditional RAG — Splits documents into chunks, embeds them, and retrieves the chunks most similar to the query — fast, but blind to relationships and meaning.
Ontology RAG — Retrieves entities, relationships, and definitions from the knowledge graph, so the connected context a question needs is already linked.
Multi-hop questions — Ontology RAG traverses relationships across the graph; traditional RAG sees only isolated, similarity-matched passages.
Consistency — Ontology RAG uses the same metric and rule definitions as your analytics; traditional RAG has no shared business meaning.
Explainability — Ontology RAG traces every answer to defined nodes and sources; traditional RAG cites a chunk, not a concept.
When ontology-grounded retrieval wins
The gap shows up the moment a question spans more than one thing. "Which open contracts are exposed to this supplier through these products?" is a graph traversal for ontology RAG and an impossible guess for chunk-based retrieval. Grounding in connected, governed meaning is what makes enterprise answers accurate, consistent with your reporting, and auditable — and it is why Scrydon's Enterprise RAG is ontology-grounded by default.
Ontology RAG vs traditional RAG vs a vanilla LLM
The same question, grounded three different ways. Ontology RAG retrieves connected meaning from your knowledge graph; traditional RAG retrieves similar text; a vanilla LLM answers from training memory alone.
| Capability | Scrydon | Traditional RAG | Vanilla LLM |
|---|---|---|---|
| Retrieval unit | Entities & relationships from the knowledge graph | Text chunks by vector similarity | Nothing — answers from model memory |
| Multi-hop reasoning | Yes — traverses relationships across the graph | Limited — isolated chunks only | No grounding to reason over |
| Business meaning | Shared ontology definitions, consistent with analytics | None — surface text similarity | Generic, not your business |
| Citations & provenance | Traces to defined concepts and source data | Cites the retrieved chunk | No citations |
| Hallucination risk | Low — grounded in verified, connected data | Moderate — depends on chunk quality | High — ungrounded |
| Best fit | Connected enterprise questions needing accuracy and audit | Simple document Q&A over flat corpora | General knowledge, no enterprise grounding |
"Traditional RAG" here means standard chunk-and-embed retrieval-augmented generation. Comparison is Scrydon's summary for orientation; real-world results depend on data and configuration.
Frequently asked questions
What is the difference between ontology RAG and traditional RAG?+
Is ontology RAG the same as GraphRAG?+
Does traditional RAG still have a place?+
How does ontology RAG lower hallucination?+
How do I use ontology RAG on Scrydon?+
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