51–58 of 58 posts
In the nicest possible way, and from a position of ignorance of the "Semantic Web": is anyone actually doing anything with these technologies outside of academia?
In the nicest possible way, and from a position of ignorance of the "Semantic Web": is anyone actually doing anything with these technologies outside of 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".
Earlier quoted context omitted.
right, but they have a close association as OP says, and RDF is wrongly dismissed for similar reasons as XML (too complex, too much ceremony, too difficult, not enough value)
As somebody who worked with RDF and SPARQL for several years, none of these are actually true - RDF is very simple to work with (especially if you avoid XML stuff), can be operated on by basic string processing tools, and is conceptually pretty simple once you get into the right mindset. I think it's just suffering from bad documentation being overexposed and good examples under-exposed.
I am glad to see this as well. I decided to use RDF for my personal project because it was well specified, has many implementations, and a human readable syntax. In the end, it is just data but I wanted to make it as accessible as possible. Does this mean that RDF is always the right choice? No, but it worked for my use case. I wish there were more choices in the open source Triplestore space with good OWL2 support b…
My impression is that the trade off when choosing RDF vs a property graph when trying to model graph data is between maximal schema flexibility and the ability to infinitely break apart the data model down to the smallest atomic structures because literally everything is a node that is either an IRI(as unique identifier) or a primitive. Vs the convenience of having more complex nodes and edges with some structure bui…
In the nicest possible way, and from a position of ignorance of the "Semantic Web": is anyone actually doing anything with these technologies outside of academia?
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 exampl…
Earlier quoted context omitted.
As well as a nigh tabular form in "N-triples" and "N-quads" https://www.w3.org/TR/n-triples/ https://www.w3.org/TR/n-quads/ which can end up as the easiest formats to work with sometimes. There are tools like 'rapper' and 'serd' to convert to and from the various formats. https://librdf.org/raptor/rapper.html https://drobilla.net/software/serd.html
The problem is that they aren’t tabular and the examples they give which make them look simple are incomplete. For example, they rarely show examples that specify the language or data type. A truly tabular format is hextuples. https://github.com/ontola/hextuples
My dabbling in that direction stopped at five (name-spaced) identifiers.
Effectively extending n-quads with an "edge" identifier. Mainly to eliminate blank-nodes but it handed the optional language & typing just fine and more importantly for my use case allowed for the uri to be expressed as curie (namespace) and a local_id each in their own column.
In the nicest possible way, and from a position of ignorance of the "Semantic Web": is anyone actually doing anything with these technologies outside of academia?
Earlier quoted context omitted.
As somebody who worked with RDF and SPARQL for several years, none of these are actually true - RDF is very simple to work with (especially if you avoid XML stuff), can be operated on by basic string processing tools, and is conceptually pretty simple once you get into the right mindset. I think it's just suffering from bad documentation being overexposed and good examples under-exposed.
Can you please recommend me an intro tutorial? I’m trying to query DataCommons and it’s really hard to figure out even simple queries.
Which has a lot of wikidata specific stuff but allows to get the basics.