RDF is great but it's somewhat inadvertently captured by academia.
The tooling is not in a state where you can use it for any commercial or mission critical application. The tooling is mainly maintained by academics, and their concerns run almost exactly counter to normal engineering concerns.
An engineer would rather have tooling with limited functionality that is well designed and behaves correctly without bugs.
Academics would rather have tooling with lots of niche features, and they can tolerate poor design, incorrect behavior and bugs. They care more for features, even if they are incorrect, as they need to publish something "novel".
The end result is that almost all things you find for RDF is academia quality and lots of it is abandoned because it was just part of publication spam being pumped and dumped by academics that need to publish or perish.
Anyone who wants to use it commercially really has to start from scratch almost.
I'm completely out of time or energy for any side project at the moment, but if someone wants to steal my idea: please take an llm model and fine tune so that it can take any question and turn it into a SparQL query for Wikidata. Also, make a web crawler that reads the page and turns into a set of RDF triples or QuickStatements for any new facts that are presented. This would effectively be the "ultimate information organizer" and could potentially turn Wikidata into most people's entry page of the internet.
As the article itself points out, this has been around for 25 years. It isn’t an accident that nobody does things this way, it wasn’t an oversight.
I worked on semantic web tech back in the day, the approach has major weaknesses and limitations that are being glossed over here. The same article touting RDF as the missing ingredient has been written for every tech trend since it was invented. We don’t need to re-litigate it for AI.
> The Resource Description Framework (RDF) is a method to describe and exchange graph data. It was originally designed as a data model for metadata by the World Wide Web Consortium (W3C).
Author listed RDF a couple dozen of times but didn’t define it, so:
The Resource Description Framework (RDF) is a standard model for data interchange on the web, designed to represent interconnected data using a structure of subject-predicate-object triples. It facilitates the merging of data from different sources and supports the evolution of schemas over time without requiring changes to all data consumers.
Five times in that article he says some version of “Accuracy triples”.
What does that even mean? Suppose something 97% accurate became 99.5% accurate? How can we talk of accuracy doubling or tripling in that context? The only way I could see that working is if the accuracy of something went from say 1% to 3% or 33% to 99%. Which are not realistic values in the LLM case.
(And I’m writing as a fan of knowledge graphs).
This seems to miss the other side of why all this failed before.
Rdf has the same problems as the sql schemas with information scattered. What fields mean requires documentation.
There - they have a name on a person. What name? Given? Legal? Chosen? Preferred for this use case?
You only have one id for apple eh? Companies are complex to model, do you mean apple just as someone would talk about it? The legal structure of entities that underpins all major companies, what part of it is referred to?
I spent a long time building identifiers for universities and companies (which was taken for ROR later) and it was a nightmare to say what a university even was. What’s the name of Cambridge? It’s not “Cambridge University” or “The university of Cambridge” legally. But it also is the actual name as people use it. The university of Paris went from something like 13 institutes to maybe one to then a bunch more. Are companies locations at their headquarters? Which headquarters?
Someone will suggest modelling to solve this but here lies the biggest problem:
The correct modelling depends on the questions you want to answer.
Our modelling had good tradeoffs for mapping academic citation tracking. It had bad modelling for legal ownership. There isn’t one modelling that solves both well.
And this is all for the simplest of questions about an organisation - what is it called and is it one or two things?
To adapt the saying, an engineer is talking to another engineer about is system, saying he's having issues with names. So he's thinking of using name spaces.
I really like RDF in theory, as a lot of its ideas just make sense to me:
- Using URIs to clarify ambiguous IDs and terms
- EAV or subject/verb/object representation for all knowledge
- "Open world" graph where you can munge together facts from different sources
I guess using RDF specifically, instead of just inventing your own graph database with namespaced properties, means using existing RDF tooling and languages like SPARQL, OWL, SHACL etc.
Having looked into the RDF ecosystem to see if I can put something together for a side project inspired by https://paradicms.github.io, it really feels like there's a whole shed of tools out there, but the shed is a bit dingy, you can't really tell the purpose of the oddly-shaped tools you can see, nobody's organised and laid things out in a clear arrangement and, well, everything seems to be written in Java, which shouldn't be a huge issue but really isn't to my taste.
> The Big Picture: Knowledge graphs triple LLM accuracy on enterprise data. But here’s what nobody tells you upfront: every knowledge graph converges on the same patterns, the same solutions. This series reveals why RDF isn’t just one option among many — it’s the natural endpoint of knowledge representation. By Post 6, you’ll see real enterprises learning this lesson at great cost — or great savings.
If you really want to continue reading and discuss this kind of drivel, go ahead. RDF the "natural endpoint of knowledge representation" right. As someone having worked on commercial RDF projects at the time, after two decades of pushing RDF by a self-serving W3C and academia until around 2018 or so, let's say I welcome people having come to their senses and are back at working with Datalog and Prolog. Even as a target for neurolinguistics and generation by coding LLMs does SPARQL suck because of its idiosyncratic, design-by-comittee nature compared to the minimalism and elegance of Prolog.
RDF provides a very natural layer for AI systems. Today, LLM-based AI systems are fundamentally challenged by hallucinations, which makes RDF-based knowledge graphs—constructed using Linked Data Principles—a powerful complement. By using hyperlinks to denote edges and nodes, these graphs enable context enrichment through ontology lookups combined with reasoning and inference.
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[ 3.3 ms ] story [ 18.7 ms ] threadThe tooling is not in a state where you can use it for any commercial or mission critical application. The tooling is mainly maintained by academics, and their concerns run almost exactly counter to normal engineering concerns.
An engineer would rather have tooling with limited functionality that is well designed and behaves correctly without bugs.
Academics would rather have tooling with lots of niche features, and they can tolerate poor design, incorrect behavior and bugs. They care more for features, even if they are incorrect, as they need to publish something "novel".
The end result is that almost all things you find for RDF is academia quality and lots of it is abandoned because it was just part of publication spam being pumped and dumped by academics that need to publish or perish.
Anyone who wants to use it commercially really has to start from scratch almost.
Hopefully version 1.2 which addresses a lot of shortcomings should officially be a thing this year.
In the meantime you can take a look at some of the specification docs here https://w3c.github.io/rdf-concepts/spec/
I'm completely out of time or energy for any side project at the moment, but if someone wants to steal my idea: please take an llm model and fine tune so that it can take any question and turn it into a SparQL query for Wikidata. Also, make a web crawler that reads the page and turns into a set of RDF triples or QuickStatements for any new facts that are presented. This would effectively be the "ultimate information organizer" and could potentially turn Wikidata into most people's entry page of the internet.
I worked on semantic web tech back in the day, the approach has major weaknesses and limitations that are being glossed over here. The same article touting RDF as the missing ingredient has been written for every tech trend since it was invented. We don’t need to re-litigate it for AI.
> The Resource Description Framework (RDF) is a method to describe and exchange graph data. It was originally designed as a data model for metadata by the World Wide Web Consortium (W3C).
https://www.wikipedia.org/wiki/Resource_Description_Framewor...
The Resource Description Framework (RDF) is a standard model for data interchange on the web, designed to represent interconnected data using a structure of subject-predicate-object triples. It facilitates the merging of data from different sources and supports the evolution of schemas over time without requiring changes to all data consumers.
What does that even mean? Suppose something 97% accurate became 99.5% accurate? How can we talk of accuracy doubling or tripling in that context? The only way I could see that working is if the accuracy of something went from say 1% to 3% or 33% to 99%. Which are not realistic values in the LLM case. (And I’m writing as a fan of knowledge graphs).
Rdf has the same problems as the sql schemas with information scattered. What fields mean requires documentation.
There - they have a name on a person. What name? Given? Legal? Chosen? Preferred for this use case?
You only have one id for apple eh? Companies are complex to model, do you mean apple just as someone would talk about it? The legal structure of entities that underpins all major companies, what part of it is referred to?
I spent a long time building identifiers for universities and companies (which was taken for ROR later) and it was a nightmare to say what a university even was. What’s the name of Cambridge? It’s not “Cambridge University” or “The university of Cambridge” legally. But it also is the actual name as people use it. The university of Paris went from something like 13 institutes to maybe one to then a bunch more. Are companies locations at their headquarters? Which headquarters?
Someone will suggest modelling to solve this but here lies the biggest problem:
The correct modelling depends on the questions you want to answer.
Our modelling had good tradeoffs for mapping academic citation tracking. It had bad modelling for legal ownership. There isn’t one modelling that solves both well.
And this is all for the simplest of questions about an organisation - what is it called and is it one or two things?
Coincidentally, my main point in any conversation about UML I've ever had
To adapt the saying, an engineer is talking to another engineer about is system, saying he's having issues with names. So he's thinking of using name spaces.
Now he has two problems
- Using URIs to clarify ambiguous IDs and terms
- EAV or subject/verb/object representation for all knowledge
- "Open world" graph where you can munge together facts from different sources
I guess using RDF specifically, instead of just inventing your own graph database with namespaced properties, means using existing RDF tooling and languages like SPARQL, OWL, SHACL etc.
Having looked into the RDF ecosystem to see if I can put something together for a side project inspired by https://paradicms.github.io, it really feels like there's a whole shed of tools out there, but the shed is a bit dingy, you can't really tell the purpose of the oddly-shaped tools you can see, nobody's organised and laid things out in a clear arrangement and, well, everything seems to be written in Java, which shouldn't be a huge issue but really isn't to my taste.
For the interested: resource description framework.
> The Big Picture: Knowledge graphs triple LLM accuracy on enterprise data. But here’s what nobody tells you upfront: every knowledge graph converges on the same patterns, the same solutions. This series reveals why RDF isn’t just one option among many — it’s the natural endpoint of knowledge representation. By Post 6, you’ll see real enterprises learning this lesson at great cost — or great savings.
If you really want to continue reading and discuss this kind of drivel, go ahead. RDF the "natural endpoint of knowledge representation" right. As someone having worked on commercial RDF projects at the time, after two decades of pushing RDF by a self-serving W3C and academia until around 2018 or so, let's say I welcome people having come to their senses and are back at working with Datalog and Prolog. Even as a target for neurolinguistics and generation by coding LLMs does SPARQL suck because of its idiosyncratic, design-by-comittee nature compared to the minimalism and elegance of Prolog.
For a detailed post on this synergy, see: https://www.linkedin.com/pulse/large-language-models-llms-po...
Disclaimer: I am the Founder & CEO of OpenLink Software, creators of Virtuoso.