RDF and related technologies are heavily used in healthcare and scientific fields, as well as industry.

The "semantic web" or "open linked data" concepts never really took off the way people had hoped, but there's till a ton of utility in the underlying standards so you'll tend to find it wherever you need complex, flexible schemas that with good interoperability between different entities.

Wikidata (a Wikimedia Foundation project) is an RDF dataset with a SPARQL querying service available at https://query.wikidata.org/

Defense industry, part of ARTT (Acquisition Requirements for Training Transformation, ), which is an incredibly-overdue-effort to merge specs. It's also being used to draft MBSE schemas for SysML, SysML has undefined overlap with the many many many other architect tools, and it's going to be the main player for MBSE (maybe . . there's some fighting about that).

These so-called "semantic web" technologies seem to come into their own when there's large scale organizations interfacing without a common reference frame. Like one org that does a spec from a programmer standpoint, and another org does one from a formal linguistics standpoint, then they have to integrate. For example, the USDoD Logistics steering group makes a spec for parts data from their requirements based on MTTF, cost, sparing, shelved space. USN makes a spec for parts data based on burn rate, transport, fuel type. It goes on and on like this, repeat a few dozen times, and you have a dump truck full of specs doing the same thing. See where I'm going here? They're speccing out the same thing from their own ivory towers, and - here's the kicker for those trying to LLM their way out of the situation - none of them are going to show their data to anyone else. The only thing that's exposed is the semantics. ARTT/CredEng is - or was, I am not sure if the program OR CredReg is still healthy - trying to solve this by unifying the semantics.

Ultimately someone's got to come along and give all these people a kick in the pants, one way or the other. You can't just float a boat around the ocean with no missiles, not these days.

It's pretty common in healthcare data, or at least the kind that deals with breadth of patient data. When trying to build knowledge about a disease by looking at a lot of patients, it's rare to get much useful info from a single source. Re-associating that multi-source data lends itself to a graph. If the company has been around for a little while, even if the customer-facing products don't use a graph database, at some point somebody has certainly tried it. (And once somebody has tried it, it lives forever in some part of the organization.)

EU data including all regulations and delegated acts and publications are searchable by sparql

https://data.europa.eu/data/sparql?locale=en#

I did struggle to find what I wanted. It's a labyrinth of metadata, and I was looking for the structured regulation text itself. In the end I stuck with good old fashioned XHTML scraping

Short answer is no. Spent many years (>10) listening to people explain how semantic technologies would transform Academic Publishing and make research more useful. Failure in my opinion because - nobody valued it enough to pay to have it done correctly, academic papers have a shelf life, academic papers often contain inaccurate information. Academics publish because it is required not because they have useful info to communicate. In most cases it is the least pleasant part of research.

I have seen many more useful tools come out of LLM in the short time it has been available than the entire 10 years working with academics using RDF, Ontologies etc. RDF is too difficult to use and has inadequate tooling. LLM is only going to get better.

Yes, pretty much all knowledge management in biology is built upon technologies that came from the semantic web community, and biology is certainly not just academia.

There's not much application for knowledge graphs in e.g. a CRUD app of customer names and addresses, but turns out there are an unlimited number of things you can describe about e.g. a protein, and you can't just design one schema because you don't know how it's going to be queried.

See: https://bioregistry.io for countless examples of public datasets used everywhere from academia to "big pharma".

Adobe developed the XMP metadata format that embeds RDF packets inside almost any kind of file. It is heavily used in Adobe products and also others, see

https://ontology2.com/essays/LookingForMetadataInAllTheWrong...

I'm using it in a personal project. I wanted something extensible, and ad hoc, and RDF is certainly that. But it's also typed, which is nice. I can add my own types (I may do that, not sure yet).

I am not well versed in the other RDF technologies. I haven't paid any attention to ontologies, or OWL or any of that stuff. I just use raw RDF, and defined my own vocabularies for everything, including structure. For example, I have my own type property. RDF also has one, but I just made my own. I have my own structure system to mostly bring order to how things are displayed, or created, etc. I am pretty much as far from the semantic web as you can get.

Since everything is "just a triple" it makes it easy to share data. So, it'll be straight forward to import and export artifacts out of my system and share them with others.

And I get SPARQL "for free", so even after new data structures are added, they're still queried like the first class ones the tool already knows about. SPARQL is pretty neat.

At the moment, I have a mostly complete RDF CRUD tool, with some first class interface prototypes (by first class I mean I have forms and UI specifically for those data types, rather than a generic resource form), and really like working with it. My DB has about 3.5M triples in it.

FWIW, RDF does enable a lot of research into useful things, so even though those cases are "academic", they aren't w/o practical outcomes as the subtext of the question implies.

RDF is great for annotating protein interactions, for example.

The schema.org markup that goes into websites for SEO and smart snippets in search engines is all RDF, usually as JSON-LD. Millions of people use RDF every day without even knowing it.

https://developers.google.com/search/docs/appearance/structu...

It's not nothing, even did a consulting gig years ago. Also, some non-profits such as Wikidata are putting it to good use I guess. But not everything benefits from representation as graph; for example, statistical data. Then there's always the unanswered question who's going to publish data without economical benefit when the money is, at best, in attention/eyeballs, or selling individual queries where backend tech isn't material or even visible. Do we even want to expose more machine-readable information in the age of ChatGPT?

TBL's vision for knowledge graphs is even older than the web. But should it be W3C's job to invent new tech? Does W3C's track record, legal and financial standing invite further standardization work? Their HTML and SVG charters have basically ceased working and W3C's last (final?) HTML recommendation is based on WHATWG HTML Review Draft January, 2020 [1].

[1]: https://sgmljs.net/blog/blog2303.html

The U.S. Open Data catalog [1] has all the metadata and even some data as Linked Data, same with the European Open Data catalog [2]

[1]: https://data.gov/ [2]: https://data.europa.eu/

If you’re pying a botique consultancy fat stacks to fix your megaCo’s absolute hairball data integration, the probability of RDF approaches 1 as price goes to ∞.

Trivial (as in ‘cat graphx graphy’) merging of complex data graphs is just too powerful.

We were using in production to power our machine learning already half a decade ago, in the region's highest valued startup.

Was also told by some of my colleagues that they were earning a lot consulting in the medical world with semantic data.

Yes, there are a lot of projects using RDF-adjacent technologies and graph databases. But most of them are not very hyped up, so you won't know it unless you know where to look. Amazon has graph database: https://en.wikipedia.org/wiki/Amazon_Neptune which suggests somebody is using it (besides that I know for a fact people use it)

Google's authorisation system Zanzibar (https://research.google/pubs/pub48190/) is not explicitly using SPARQL, but it's a good example of the sort of thing that a graph-based data model can do. I _think_ Zanzibar can be implemented with SPARQL: we implemented something very very close (and more powerful in some ways) with it

Accenture just invested in Stardog, leading Knowledge Graph plaform, which is based on these W3C standards. Can't get much less "academic" than Accenture.

https://newsroom.accenture.com/news/accenture-invests-in-sta...

UNESCO does some stuff with it in places. (Ocean info hub, which I did some work on). The EU likes DCAT for data syndication, which is generally serialized as RDF. They’re also promoting linked data, but I don’t really see how it’s truly useful. It mainly seems like a way to turn a bulk download into 100k api requests.

Check out https://solidproject.org (If you want a short intro I recently gave a ~30min talk about it: https://noeldemartin.com/fosdem)

Firefox uses RDF internally. Chase Bank uses it presumably.