Ontology is in every enterprise AI pitch deck now, sitting next to semantic layer, context layer, and context graph. Very few of the people selling it can tell you how you would actually build one. Casey Hart can, and his answer is far less grand than the pitch. An ontology is a list of the things your business cares about and how those things relate to each other. You can start it on a Post-it note. You can get real value from putting only your metadata in the graph, without migrating a single row of production data. And sometimes the honest answer is that you don't need one.

Casey came to ontology from a philosophy PhD, by way of a job posting on a philosophy jobs board, which landed him at Cycorp working under Doug Lenat on Cyc's own representation language years before OWL and RDF were the standards everyone argues about. That history makes him unusually good on the question underneath the buzzwords: when is a rules-and-reasons system worth the cost, and when should you just let the model guess? For a technical buyer, this is the episode that turns "you have an ontology problem" from an accusation into a scoped project.

IN THIS EPISODE

• Why "hallucination" is the wrong word, and what the brake lines on a car have to do with it

• Cyc's highly expressive representation language versus the three-word sentences of OWL and RDF, and what you actually lose in the trade

• Whether RDF-star buys you any new expressivity (Casey argues it does not, and says a W3C committee member agrees)

• What happened to Cyc, from someone who worked there

• The ontology use case nobody pitches: 5,000 columns and no analyst who knows what they mean

• The metadata-first on-ramp, where you get value without migrating your data into a graph

• Who in an organization should own the ontology, and why there is no single right answer

• What to do if you are a mid-sized company with no knowledge management function at all

• Extending SNOMED or any off-the-shelf ontology, framed as a house renovation

• Open world versus closed world, and why engineers trained on schema design get it backwards

KEY TAKEAWAYS

• The model was not malfunctioning when it invented a fact. It was performing exactly as intended. It generates text, and it did. Calling that a hallucination makes it sound more sophisticated than it is.

• An ontology is a summary of the stuff your business cares about and how those things relate.

• You are not on the hook for migrating all your data into a graph. Put the metadata in the graph and you can get value out of it immediately.

• If your data always looks the same and you always do the same thing with it, don't build an ontology. Write the program.

• Start by saying true things, in general terms, knowing you will refine them later. Leave a slot for the detail you don't need yet.

Casey Hart is an ontologist and consultant. He earned a PhD in philosophy and found his way into the field through a posting on a philosophy jobs board, which took him to Cycorp, where he worked under Doug Lenat on Cyc's own representation language, in the years before OWL, RDF, and knowledge graphs became standard vocabulary. He has since worked in big tech, including a stint at Amazon, and now builds ontologies for his own consulting clients. He hosts the Ontology Explained channel on YouTube and co-hosts a new podcast, Philosophy, Programs and Prompts, with Carl of Internet of Bugs.

LINKS

Casey Hart | Ontology Explained

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ContextOps is hosted by Vivek Khandelwal with co-host Joshua Thomas.