GraphRAG · Ontology
Why ontology-first GraphRAG beats naive chunking
Structured knowledge gives retrieval a spine—here is how we think about entities, relations, and prompts when building on a graph.
Retrieval-augmented generation over documents alone often drifts: the model sees overlapping chunks and misses the constraints that humans use to reason. An ontology-first approach anchors answers in typed entities and relations before you ever ask the LLM to summarize.
Entities are not just strings
When products, regulations, and internal policies are modeled as nodes with stable IDs, you can traverse the graph for multi-hop questions without praying that two PDF chunks happen to sit next to each other in the index.
If your graph does not know what a “clause” is, your retriever will keep fetching paragraphs that mention the word without understanding obligations.
Practical takeaway
- Define core types and relations for your domain.
- Attach evidence spans to graph edges or nodes, not only to loose chunks.
- Measure retrieval with tasks that require joins across documents.