Reasoning
Most people don’t notice because they don’t know what it is. A reasoning engine is a system that derives facts you never explicitly stored. For instance, the sub-classing idea is transitive, it knows that if Mammal is a subclass of Animal, and Dog is a subclass of Mammal, then Dog is also a subclass of Animal. This is revealed without you asserting it. Given a rule that says “a person who authored a paper at institution X was affiliated with X,” it infers the affiliation. You assert the minimum; the engine closes the world (infers the unspecified).
This is what OWL entailment and RDFS semantics gave RDF triple stores in 2004. Formal, auditable, reproducible inference. Load a consistent ontology, run a reasoner like HermiT or Pellet, and your graph knows more than you told it.
Property graphs (Neo4j, Memgraph, Kuzu, TigerGraph…) have none of this. You write Cypher. You get back exactly what you put in. The graph cannot derive a single fact that wasn’t explicitly asserted. The schema is flexible and the tooling is excellent, but the semantics stop at the storage boundary. If necessary people used to create it in the business logic (layer).
Nowadays everyone is wiring LLMs on top of property graphs and calling it “AI-powered knowledge reasoning.”
Well, it isn’t. The LLM is guessing the inference layer inconsistently, expensively, without auditability, and without any guarantee of logical consistency. It depends on the (size of) model, how it fits the data, the language and more. A reasoning engine gives you closed-world guarantees. An LLM gives you a confident guess.
The reasoning gap in labeled graphs is not a footnote. It is the central unsolved problem in production knowledge graph deployments and almost nobody in the LPG ecosystem is talking about it. It’s inconvenient.
Don’t get me wrong, I worked with and for Neo4j, with and for TigerGraph. I like LPGs and advise customers to go for it, if I am convinced it’s the right choice. What I try to emphasize is what you get and what you think you get out of LPGs via LLMs.
OWL Implementations: https://lnkd.in/eVXHkjYn