Have you ever written a SPARQL query that returned a suspiciously large amount of results, especially with too many combinations of values? You may have accidentally requested a cross product. I have spent too much time debugging queries where this turned out to be the problem, so I wanted to talk about avoiding it.
Last month I wrote about how we can treat the growing amount of JSON-LD in the world as RDF. By “treat” I mean “query it with SPARQL and use it with the wide choice of RDF application development tools out there”. While I did demonstrate that JSON-LD does just fine with URIs from outside of the schema.org vocabulary, the vast majority of JSON-LD out there uses schema.org.
I’ve been using the curl utility to retrieve data from SPARQL endpoints for years, but I still have trouble remembering some of the important syntax, so I jotted down a quick reference for myself and I thought I’d share it. I also added some background.
When I wrote Semantic web semantics vs. vector embedding machine learning semantics, I described how distributional semantics–whose machine learning implementations are very popular in modern natural language processing–are quite different from the kind of semantics that RDF people usually talk about. I recently learned of a fascinating project that brings RDF technology and distributional semantics together, letting our SPARQL query logic take advantage of entity similarity as rated…
After I wrote about Extracting RDF data models from Wikidata in my blog last month, Ettore Rizza suggested that I check out wdtaxonomy, which extracts taxonomies from Wikidata by retrieving the kinds of data that my blog entry’s sample queries retrieved, and it then displays the results as a tree. After playing with it, I’m tempted to tell everyone who read that blog entry to ignore the example queries I included, because you can learn a lot more from wdtaxonomy.