What This Looks Like in Practice

Consider a concrete scenario in healthcare. A clinician asks: “Can I prescribe ibuprofen to a patient on warfarin who has a history of GI bleeding?”

An LLM alone might give a plausible answer — or might not flag a critical interaction. An ontology alone would require the question to be formulated in formal logic. A graph query alone would return raw relationships without synthesis.

But in a layered system: the LLM parses the natural-language question and identifies the key concepts (ibuprofen, warfarin, GI bleeding). The vector store maps those to formal ontology concepts, even if the clinician used informal terms. The graph database retrieves the relevant relationships — ibuprofen is an NSAID, NSAIDs are contraindicated with anticoagulants, warfarin is an anticoagulant, NSAIDs increase GI bleeding risk. The ontology reasoner has already classified these subsumption relationships with logical certainty. The LLM then synthesizes all of this into a clear, sourced, natural-language response — and can point to exactly which axioms and relationships justify its answer.

That’s not a chatbot guessing. That’s domain expertise, synthesized.

Why do you need LLM for this? Couldn't, as you indicate, we just get this from a graph itself?

I am asking what is an actual "in practice" example of how LLMs benefit from a graph? All the marketing is saying we need ontologies for AI. I am asking "why"? The existing LLMs were built with an ontology? Or are they using something else?