[semihsalihoglu]

I wrote a blog post on something that I find is often misunderstood and under-appreciated: Resource Description Framework (RDF). I explain what RDF is, what it is not, when you may need it, and its virtues and vices. RDF is good to know about in our increasingly AI-dominated world, since it has its roots in knowledge representation and reasoning (KRR), which is a field of AI, known as good-old-fashioned symbolic AI.

I explain RDF first as a data model and compare its pros and cons with relational and property graph model. I then explain RDF and the standards around RDF, such as RDFS and OWL, as a "knowledge representation system". I cover RDF's roots in knowledge representation and reasoning (KRR), traditional symbolic AI systems. I also discuss several directions I have seen people pursue to improve LLMs with RDF-based or more broadly KRR-based technology (see especially the link to Doug Lenat's last article (I think) on the subject before he passed on).

It's a bit of a long read but I hope people find it useful to think about RDF.

[compressedgas]

Data integrity is what SHACL https://www.w3.org/TR/shacl/ is for. (OWL is for inferencing. SHACL is for integrity.)

[semihsalihoglu]

That's correct though OWL also provides ways to constraint what can be encoded (e.g., the cardinality constraint example I gave). But yes, SHACL is primarily for constraints. I general, there are several other standards than RDFS and OWL I didn't mention in the post. I wanted to give a few example standards to explain to show how RDF + standards forms something more than a regular data model that developers think of.