I think it's a journey a lot of us have gone on, it's an appealing idea until you hit a variety of really annoying cases and where you are depends on how you end up trying to solve it. I'm maybe being unfair to the academic side but this is how I've seen it (exaggerated to show what I mean hopefully).

The more academic side will add more complexity to the modelling, trying to model it all.

The more business side will add more shortcuts to simplify the modelling, trying to get just something done.

Neither is wrong as such but I prefer the tendency to focus on solving an actual problem because it forces you to make real decisions about how you do things.

I think being able to build up knowledge in a searchable way is really useful and having LLMs means we finally have technology that understands ambiguity pretty well. There's likely an excellent place for this now that we can model some parts precisely and then add more fuzzy knowledge as well.

> The big question I still have is whether RDF offers any significant benefits for these way more limited scopes. Is it really that much faster, simpler or better to do queries on knowledge graphs rather than something like SQL?

I'm very interested in this too, I think we've not figured it out yet. My guess is probably no in that it may be easier to add the missing parts to non-rdf things. I have a rough feeling that actually having something like a well linked wiki backed by data sources for tables/etc would be great for an llm to use (ignoring cost, which for predictions across a year or more seems pretty reasonable).

They can follow links around topics across arbitrary sites well, you only need more programmatic access for aggregations typically. Or rare links.