Hi!
https://everypageispageone.com/2011/07/13/search-vs-query/ ). Currently our query service is a very strong and complete service, but Wikidata search is very poor. Let's take Blade Runner.
I don't think it's *very* poor anymore, but it certainly can be better.
In my ideal world, everything I see as a human gets indexed into the search engine preferably in a per language index. For example for Dutch
Err.... The problem is that what you see as a human and what search engine uses for lookups are very different things. While for text articles it is similar, for structured data it's quite different, and treating structured data the same way as text is not going to produce good results, partially because most search algorithms make assumptions that come from text world, partially because we'd be ignoring useful clues present in structured data.
something like a text_nl field with the, label, description, aliases, statements and references in there. So index *everything* and never see
There are such fields, but it makes no sense to put references there, because there's no such thing as "Dutch reference". References do not change with language.
a Qnumber or Pnumber in there (extra incentive for people to add labels in their language). Probably also everything duplicated in the text
That presents a problem. While you see "instance of": "human", the data is P31:Q5. We can, of course, put "instance of": "human" in the index. But what if label for Q5 changes? Now we have to re-index 10 million records. And while we're doing it, what if another label for such item changes again? We'd have to start another million-size reindex. In a week, we'd have a backlog of hopeless size, or will require processing power that we just don't have. Note also that ElasticSearch doesn't really do document updates - it just writes a new document. So frequent updates to the same document is not its optimal scenario, and we're talking about propagating each label edit to each item that is linked to that one. I'm afraid that would explode on us very quickly.
The problem is not indexing labels, the problem is keeping them up-to-date on 50 million interlinked items.
When displaying, it's easy - you don't need to worry until you show it, and most items are shown only rarely. Even then you see a label out of date now and then. But with search, you can't update label on use - when you want to use it (i.e. look up), it should already be up-to-date, otherwise it's useless.
As for implementation: We already have the logic to serialize our json to the RDF format. Maybe also add a serialization format for this that is easy to ingest by search engines?
I don't know any such special format, do you? We of course have JSON updates to ElasticSearch, but as I noted before, updates are the problem there, not format. RDF of course also does not carry denormalized data, so we also update only entries that need updating, and fetch labels on use. We can not do it for search index. I don't think format here is the problem.
. Making it easier to index not only for our own search would be a nice added benefit.
Sure, but experience have shown that the strategy of "dump everything into one huge text" works very poorly in Wikidata. That's why we implemented specialized search that knows about how the structured data works. If the search sucks less now than it did before, that's the reason.
How feasible is this? Do we already have one or multiple tasks for this on Phabricator? Phabricator has gotten a bit unclear when it comes to Wikidata search, I think because of misunderstanding between people what the goal of the task is. Might be worthwhile spending some time on structuring that.
Wikidata search tasks would be under "Wikidata" + "Discovery-Search". There are multiple tasks for it, but if you want to add any, please feel welcome to browse and add.