About the meaning of an ontology
Ask a scientist “what’s your ontology?” and you get a 50-year old fight about whether the wavefunction is real. Ask a knowledge graph engineer “what’s your ontology?” and you get a class hierarchy with three subclasses and a cardinality constraint.
Same word but wildly different perceptions.
In science, “ontology” means: what does the math actually correspond to, out there, in reality? Is the quantum state the physical thing itself (wavefunction realism)? Or just an agent’s belief about a sparser underlying reality? People have built entire careers, and several genuinely competing theories, purely on disagreeing about this. It’s how science works.
In knowledge graphs, we call a SKOS taxonomy or an OWL class tree an “ontology” and move on with our day. No one’s losing sleep over whether ‘:hasPart’ carves reality at the joints.
The question is subtle but worth considering over a few cups of coffee: is this structure describing the world, or just organizing our data about it?
A real ontology commits to what exists and why. Most of what we develop is a schema with a bit of delusion. It’s useful, sure, but it rarely answers the question ‘what is it actually doing?’. Maybe that’s fine, maybe it isn’t. I’m only challenging you a little.
Every enterprise KG project eventually hits the same wall: two teams model “Customer,” “Product,” or “Risk” differently, and nobody can say which one is right because nobody was ever forced to commit to what those things actually are. You end up with a schema that’s locally consistent and globally incoherent. It has perfectly valid SHACL shapes that quietly contradict each other the moment you try to federate across departments, or external partners.
If your “Customer” entity is just a convenient bucket for whatever fields the CRM team needed this quarter, you’ll ship fast and pay for it later in entity resolution hell, broken joins, and AI agents hallucinating relationships that were never really there. If you instead force an actual ontological commitment (what is a Customer, independent of which system is asking ) you get something that survives contact with a merger, a new product line, or a regulator asking “are these the same risk or two different ones?”. Of course, I know I am advocating the long road here. It means thinking, confronting diverging opinions, changing points of view. This is where the business world can learn something from the scientific method.