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Indeed, I often get the impression that (young) academics want to model the entire world in RDF. This can't work because the world is very ambiguous.

Using it to solve specific problems is good. A company I work with tries to do context engineering / adding guard rails to LLMs by modeling the knowledge in organizations, and that seems very promising.

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?

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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.

The academic / business divide is a great example of the correct model depending on what you want to do. The academic side wants to understand, the business side wants to take action.

For example, the Viable System Model[1] can capture a huge amount of nuance about how a team functions, but when you need to reorganize a disfunctional team, a simple org chart and concise role descriptions are much more effective.

[1] https://en.wikipedia.org/wiki/Viable_system_model

Which company? I need to build an enterprise knowledge graph.

A small startup in the Netherlands, but they're very much searching for approaches themselves, I don't think they can help you right now.