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.
I’ve been thinking about which machine learning tools can contribute the most to the field of digital humanities, and an obvious candidate is document embeddings. I’ll describe what these are below but I’ll start with the fun part: after using some document embedding Python scripts to compare the roughly 560 Wikibooks recipes to each other, I created an If you liked… web page that shows, for each recipe, what other recipes were calculated to be most similar to that…
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.