@Ramsey-WMF This is basically what we can get "for free" from elasticsearch as related-structured data. This counts and displays the top N structured data statements over the search results set.

I wrote a basic python script to modify our existing queries and generate the suggestions and appended few examples of real search queries. Generally this seems to run into a bit of a data incompleteness issue. A query might have 2k results but only 3 of them have structured data, so you get whatever happens to be there. Might be acceptable though, i dunno?

query: flowers

Query: mimas cassini

Query: french kings portraits

Query: dog

Query: terrier

Hey Erik. Thanks! This is a good start. I'm interested to learn a bit more about how it works and how much we can tweak it, but there do definitely seem to be some logistical limitations that we might not even be able to solve with more data on more files. Still, something is better than nothing! 😄

Adding Amanda and some extra tags so we can track this better

Script used to collect above results. Note this needs access to elasticsearch directly as cirrussearch does not yet support this query: P8829

I suppose there is also the significant terms aggregation, it's similar to the aggregation above but this tries to take into account the frequency in the total document collection vs the frequency in the result set. Essentially this orders structured data statements by how much more likely they are to be found in the result set vs the overall document collection:

Query: flowers

Query: mimas cassini

no results

Query: french kings portraits

no results

Query: dog

Query: terrier

Hey @Ramsey-WMF - is this enough info for you and the team or do you need more from us? Thanks!

This is good for where we are now. As you know, we're looking at longer term systems to do this in a better way, but the script that Erik provided gives us something to work with in the meantime. Thanks!

Closing because we're tackling this with a different approach using concept chips in T256431.