Resource Description Framework

But we hardly ever refer to it as a graph.

It's interesting because you know how in the RDF world, everything is statements and like triples.

And when you chain triples, they form graphs, right? But this kind of notion of it, and it makes perfect sense because the focus back then was, hey, we have this amazing thing, which is the web, but it's something that's built for human consumption through web browsers.

Now, what if we do something to it and enrich it with some machine-readable version of the same information that then agents will be able to use to do smart things? And you probably remember the 2001 article, The Scientific American, by Berners-Lee, Oral Asila.

What if we take that data at scale and put it in data stores and databases? And that's where Specialized, and we'll get there, stores for this type of information appeared.

But at the beginning, it was even about annotating.

If you remember, there were formats of RDF that were about annotating web pages.

So you would have Hey, I'm Jesus and I'm based in Madrid.

But then there was a file called ontology dot, you know, YAML or whatever, and it basically had entities, descriptions of entities and a list of relationships as kind of a super basic YAML file.

It had nothing to do with an RDF kind of owl style of ontology, which in a way I don't think that's necessary.

It's not like the concept of ontology requires format.

I don't think that's the case.

And so we shouldn't dumb it down, right? We shouldn't, you know, as I said in my talk, we shouldn't take a glossary which is a term in the definition of pretend it's an ontology, because it's not even a cheap ontology, right? It doesn't follow the rules of an ontology.

Yeah, it doesn't even contain all the information that an ontology should.

But also, as soon as we bind technology and very technical technology like L and RDF to ontologies and graphs, we make it even more complex.

So if you told me an ontology is controlled vocabulary and a taxonomy and a thesauri and then that bundle of things, relationships, and very specific language around the relationships that are effectively rules that cannot be broken, not just vague statements.

You can build the map from scratch, or you can generate it from an existing semantic model.

You can even import definitions written in open standards like RDF and OWL.

That last option matters for teams who already modeled their business somewhere else, because their work can come along instead of being redone.

Sometimes the path between things is the whole point, and that's where the optional graph layer comes in.

The W3C recently published version one zero of the YAML LD specification.

YAML LD is a set of conventions which specify how to serialize RDF linked data as YAML based on JSON-LD syntax, semantics, and APIs.

JSON-LD is a lightweight machine-readable format used to store and transport linked data on the web.

JSON-LD uses schema.org vocabularies to explicitly define what web page elements mean, like products, prices, or authors.

Ontology is evolving from a formal model of meaning into an operational component of knowledge graphs, decision systems, and AI agents.

The progression runs formal semantics → knowledge graphs → decisions → AI agents, or put differently, meaning → traversal → reasoning → action.

Amir Hosseini offers practical advice for ontology engineering on RDF and property graphs.

He argues that even though only RDF truly supports ontologies, ontology engineering practices remain relevant across both systems.

That work pays off when the same concepts travel across engineering, production, quality, maintenance, and enterprise data, especially when several applications need to reason from the same definitions.

RDF and OWL sit further along that formal path.

RDF represents facts as simple statements, subject, relationship, object, like a product revision requires an operation.

OWL adds formal rules about classes, properties, and logical constraints.

These standards help where semantic precision, data exchange, and automated reasoning justify the learning curve.

They aren't a badge of maturity.

If the plant just needs to answer a focused scheduling question and your team has no experience with semantic technologies, starting with a large RDF and OWL program can delay the work by months.

You may end up with elegant definitions and no answer for the planner who needs to move an order before shift change.