May I remind you all that as it is, particularly the "descriptions" are really problematic. They are often created based on Wikipedia categories and it is quite rare that they get updated. Compare this with the "automated descriptions" that have been around for years.

When new properties are added to an item, it may change the automated description as a result and, this is reflected in any language. These changed descriptions may be stored until the next update on the item, they may be generated when needed and obviously they may be cached. They may be used in the build up of a search and this will be a much bigger incentive for people to update labels.

Contrary what some think, labels are updated based on a "need", this need is hardly there because Wikidata only appeals to geeks. It is why the Reasonator approach to labelisation makes so much sense. You see the missing labels, you add them and the next item will show the new labels. Given that people work in domains, it is a sound approach and, this will also quite quickly improve the quality of "automated descriptions" in any language.

Did I tell you that I disambiguate items by adding labels and properties in Wikidata? In Reasonator when you refresh a "search" you will see for instance a date of birth death added making John Smith that John Smith,

Obviously, search could be a lot better and using "automated descriptions" will make a positive difference.

On 15 August 2018 at 07:20, Stas Malyshev <smalyshev@wikimedia.org> wrote:

> 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

> . 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.

Stas Malyshev

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