Much of the original point of the web was not just linking from one page to another but also saving and managing links, ideally with some metadata. Because of this, all browsers give you some way to save a link to a web page as a bookmark, and they typically let you sort these into a hierarchical arrangement of folders.

I recently asked on Twitter about the availability of command line OWL processors. I got some leads, but most would have required a little coding or integration work on my part. I decided that a small project that I did with the OWL-RL Python library a few years ago gave me a head start on just creating my own OWL command line processor in Python. It was pretty easy.

During the course of my recent blog posts What is RDF?, What is RDFS?, What else can I do with RDFS?, and Taxonomy management with SKOS, some readers wondered if I would do a “What is OWL?” followup. I recommended to one inquirer that he read pages 39-41 and 263 - 269 of Learning SPARQL; I think that provides a pretty good introduction to OWL’s history and how to do some of the set-based logic that was an important part of its original intent.

In 2011, IBM developerWorks published an article that I wrote titled “Improve your taxonomy management using the W3C SKOS standard.” (They have always loved those “Get Better at This Thing” titles.) Several years later they took it (and a ton of other developerWorks content) down. I have republished it here as background for recent discussions about when OWL is appropriate to use and when it isn’t; more on that next month. I didn’t change anything but added a…

In my last blog entry, What is RDFS?, I described how the RDF Schema language lets you define RDF vocabularies, with the definitions themselves being RDF triples. We saw how simple class and property name definitions in a schema can, as machine-readable documentation for a dataset’s structure, provide greater interoperability for data and applications built around the same domain. Today we’ll look at how RDF schemas can store additional kinds of valuable information to add to what we…

RDFS, or RDF Schema, is a W3C standard specialized vocabulary for describing RDF vocabularies and data models. Before I discuss it further, though, I’d like to explain why the use of standardized, specialized vocabularies (whether RDFS itself or a vocabulary that someone uses RDFS to describe) can be useful beyond the advantages of sharing a vocabulary with others for easier interoperability.

I have usually assumed that people reading this blog already know what RDF is. After recent discussions with people coming to RDF from the Linked (Open) Data and Knowledge Graph worlds, I realized that it would be useful to have a simple explanation that I could point to. This builds on material from the first three minutes of my video SPARQL in 11 Minutes.

When I saw “Add support for scripting languages other than JavaScript” in the Jena release 4.0.0 release notes my first reaction was “What? I can run the arq command line SPARQL processor and call my own functions that I wrote in JavaScript?”