Links to the literature is one of my interests, can in cases where Wikidata has this information you can start to enhance the way we display publications, e.g.
Wow, that’s great! Hope it wasn’t too tedious a slog. Oh, and I saw this list of cicadas linked to a Fauna of NZ publication https://t.co/goIG9Lr7Gs - I’m assuming you made those links? Nice example of the potential to enhance publications on @Wikidata pic.twitter.com/iDsv4YgnkF— Roderic Page (@rdmpage) April 19, 2020
The Wikidata model is very like that used in Darwin Core, where everything is a taxon and every taxon has a name, which means that relationships that are notionally between names and not taxa (e.g., basionym) are all treated as relationships between taxa.
One big challenge is how to interpret Wikidata as a classification, given that we expect classifications to be trees. The taxonomic classification in Wikidata is clearly not a tree, for example:
Hmmm, so @wikidata has a rather *complicated* biological taxonomy that is certainly not a tree. Here is the parent - child structure for the frog family Leptodactylidae. Instead of a single path from tip to root, we have all sorts of detours #crowdsourced pic.twitter.com/zu03KvLgnG— Roderic Page (@rdmpage) April 4, 2020
What I think is happening here is that different people are adding different parent taxa, depending on which classification they follow. Some classifications (e.g., that used by GBIF) are "shallow" with only a few levels (e.g., kingdom, phylum, class, order, family, genus), other classifications are deep (e.g., NCBI). So the idea of simply being able to do a SPARQL query and get a tree (e.g. Displaying taxonomic classifications from Wikidata using d3js and SPARQL) runs into problems. But this could also be a strength, particularly if we had a reference or source for each parent child pair. That way we could (a) store multiple classifications in Wikidata, and (b) have queries that retreive classifications according to a particular source (e.g., GBIF).
So, lots of potential, but lots I've still to learn.