Disclaimer: I am not a Neo4j expert, however, this article was written based on my experience with semantic triple building and with the help of ChatGPT and approved by me.
A recent article on LinkedIn argued that most graph databases — particularly Labeled Property Graphs (LPGs) — fall short of being “true knowledge graphs” because they lack native semantics or reasoning capabilities. RDF triple stores with OWL/RDFS ontologies were held up as the better choice for multi-hop inference, semantic consistency, and cross-domain integration.
This critique is certainly fair - but only partially. While LPGs don’t natively provide deductive reasoning, I’d argue they offer something equally valuable: pragmatism and control.
1. Reasoning is not always deterministic
In the RDF/ontology world, inference is logical: either a fact follows from the ontology, or it doesn’t. But in many enterprise contexts, reasoning is better understood as weighted. Connections carry strength, frequency, or confidence — e.g., a pain is mentioned in 80% of interviews, or two concepts are semantically close but not identical. Here, control over semantic proximity and match strength matters as much as strict logic.
2. External reasoning + LPG is a powerful combo
My own approach is to run reasoning externally — where I can calibrate parameters like proximity, frequency, and confidence — and then import the resulting triples into Neo4j. This way, the LPG acts as a flexible, high-performance backbone while I retain control over the quality/volume trade-off of semantic matches. In other words: the LPG doesn’t need to do all the reasoning itself; it just needs to integrate the results cleanly.
3. LPGs excel at agility and enterprise usability
Ontologies and reasoning engines are powerful, but they come with steep learning curves and performance trade-offs. Many enterprise teams don’t need full OWL inference on day one — they need a graph they can iterate on quickly, integrate with existing systems, and query in straightforward ways. Neo4j and other LPGs shine here: they let teams build working graphs fast, and enrich them semantically when the use case requires it.
4. It’s not RDF vs. LPG — it’s about balance
The strongest approach is often hybrid. Use ontologies where consistency and reasoning are essential. Use LPGs where flexibility, performance, and developer adoption matter. And don’t forget that semantics can be introduced incrementally — as parameters, weights, and external enrichments — rather than as an all-or-nothing commitment to OWL/RDFS reasoning.
In short: LPGs may not have “native semantics,” but they do have the practical agility to model enterprise reality. Paired with external reasoning, they can deliver both speed and depth — without locking teams into one paradigm. For many of us, that balance is what makes them so powerful.