Over a year ago, in Querying geospatial data with SPARQL: Part 1, I described my dream of pulling geospatial data down from Open Street Map, loading it into a local triplestore, and then querying it with queries that conformed to the GeoSPARQL standard. At the time, I tried several triplestores and data sources and never quite got there. When I tried it recently with Ontotext’s free version of GraphDB, it all turned out to be quite easy.
I had heard that Go (also known as “golang”) was an increasingly popular newish programming language before I migrated my blog from being generated by handmade XSLT scripts on snee.com to using the Hugo platform to generate it on bobdc.com. Hugo is written in Go, which was invented at Google (get it?) by three people, two of whom had contributed to the development of C, Unix, and important related technology at Bell Labs. Go provides an excellent basis for a website generation…
Something that happens to me now and then: I’ll hear that an organization with a lot of interesting data (science, music, whatever) makes the data available on a SPARQL endpoint. I send my browser to the URL listed as the SPARQL endpoint and I see a web form. I enter a simple query on the web form to retrieve a few random triples, click the form’s button, and the results of my query appear. Then I enter fancier queries to explore the endpoint’s data.
I have seen several tools for converting spreadsheets to RDF over the years. They typically try to cover so many different cases that learning how to use them has taken more effort than just writing a short perl script that uses the
split() command, so that’s what I usually ended up doing. (Several years ago I did come up with another way that was more of a cute trick with Turtle syntax.)
A few years ago I wrote a blog post titled SPARQL in a Jupyter (a.k.a. IPython) notebook: With just a bit of Python to frame it all. It described how Jupyter notebooks, which have become increasingly popular in the data science world, are an excellent way to share executable code and the results and documentation of that code. Not only do these notebooks make it easy to package all of this in a very presentable way; they also make it easy for your reader to tweak the code in a local copy of your…
I’ve often thought that named graphs could provide an infrastructure for managing inferenced triples, and a recent Twitter exchange with Adrian Gschwend inspired me to follow through with a little demo.