> Maybe this all changes when AI takes over, but AI seems to do fine without us defining ontologies, etc.
If you say "AI" in 2024, you are probably talking about an LLM. An LLM is a program that pretends to solve semantics by actually entirely avoiding semantics. You feed an LLM a semantically meaningful input, and it will generate a statistically meaningful output that just so happens to look like a semantically meaningful transformation. Just to really sell this facade, we go around calling this program a "transformer" and a "language model", even though it truthfully does nothing of the sort.
The entire goal of the semantic web was to dodge the exact same problem: ambiguous semantics. By asking everyone to rewrite their content as an ontology, you compel the writer to transform the semantics of their content into explicit unambiguous logic.
That's where the category error comes in: the writer can't do it. Interesting content can't just be trivially rewritten as a simple universally-compatible ontology that is actually rooted in meaningfully unambiguous axioms. That's precisely the hard problem we were trying to dodge in the first place!
So the writer does the next best thing: they write an ontology that isn't rooted. There are no really useful axioms at the root of this tree, but it's a tree, and that's good enough. Right?
What use is an ontology when it isn't rooted in useful axioms? Instead of dodging the problem of ambiguous semantics, the "semantic web" moves that problem right in front of the user. That's probably useful for something, just not what the user is expecting it to be useful for.
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I have this big abstract idea I've been working on that might actually solve the problem of ambiguous semantics. The trouble is, I've been having a really hard time tying the idea itself down to reality. It's a deceptively challenging problem space.