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(moderator) Good afternoon, everybody.
We're about to start.

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I'm presenting you John Samuel

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who works at the French
engineering school CPE,

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based in Lyon in France.

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And he will tell us something more

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about the translation
of properties in Wikidata.

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As you know,
as is the case in all sessions,

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there is an etherpad
for collaborative note-taking.

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Please don't forget that.

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We'll have the presentation

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and then we'll have
some time for a short Q&A.

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- The floor is yours.
- (John) Thanks, [inaudible].

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Thank you all for coming here.

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So my talk is about analyzing
translation of Wikidata properties.

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So just give you a quick outline.

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I would like to introduce this topic.

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I will present a tool
that I developed some years before,

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called WDProp,
which I'm continuously working,

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and based on the feedback
from the community,

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I add new features.

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And then I will talk about
something called coarser analysis,

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where I would like to look
at the property translation,

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from a much larger picture.

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So I will talk about
how we collected this data,

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because this work is also done
with one of my students, Thibaut Chamard.

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And then I will present some results,
and finally, I will conclude the talk.

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So Wikidata, as you all know,
it started in 2012,

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and it's a free, open, linked,
structured, collaborative,

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and multilingual knowledge base.

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My focus today
is on the multilingual part,

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because there is a big change
from the traditional way

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of how we used to edit on Wikipedia site.

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There were multiple subdomains,

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and now you'll have a single domain
on a Wikidata

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where multilingual contributors come
and write or create articles.

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So this is a collaborative.

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There has been work to say
what exactly is collaborative,

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why it is collaborative.

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I have given references for these works.

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So this is, if you see Wikidata,

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everything that starts
is starting from the property.

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The property is proposed
and then discussed and voted.

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And then it is created
and finally translated,

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and then you are finally
able to use these properties.

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But these properties may also be deleted--

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there's also something called deletion.

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But, as I highlighted on this slide,

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my focus is on the multilingual aspect,

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and the property creation
and translation point of view.

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So you have been here
for the past two days,

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and by this time
you have seen many articles,

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and I just want to point
what am I looking for on a Wikidata item.

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This is a Wikidata item,

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so you have this Q2841, which is Bogotá,

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which is the capital city of Colombia,

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and you have four parts here:

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the languages, the labels,
the description, and aliases.

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So you can see,
for different languages

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you'll have the label,
you have the description

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as well as if there any aliases
also known as, you could see them.

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And this, under the city,
where you see the labels

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and the properties together.

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This is Avignon, a city in France.

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So what I'm interested in
is only the properties part.

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For example, official name, native label,
country, capital of, et cetera.

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So when I say property,
for example, if a country,

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in this country,
I'm looking at different aspects:

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the language, the label,
and the description,

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and see how things change.

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For example, if you take <i>instance of</i>--

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okay, everybody knows instance of,
you have been using it quite a lot--

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this is P31, you see
the number of aliases in English

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for the property P31 in instance of,

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and then you would find
that these types of properties

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are created after discussion
with the community.

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So if I take the complete prop--
the procedure,

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what happens to creation of properties--

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you start proposing properties
with some possible translation.

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It is important it's not just in English.

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You have the templates
to suggest your properties

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in your local language.

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So that's why it's a proposition
with possible translation.

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And then you put it to discussion,
then you are put to voting,

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and it's created, and then finally,
the community members start translating it

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and people put it into use.

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But then you cannot be guaranteed
the properties that are created

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are always there forever.

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Properties can be deleted,
just like items can be deleted.

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But then, again,
it goes through a similar procedure.

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You put the property

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as you propose that it should be deleted,

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and if the community decides it,
it votes it, and then if it is decided--

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the majority votes
has decided to delete it--

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we deprecate the property,
and finally we delete this property.

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So for today's talk, I'm mostly interested
for the translation part.

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So where are the translations happening?

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First, the translation would happen
at the proposition part,

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and then you could find that,
at the time of creation,

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the person who creates the property
can use the exact names

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that were suggested
by the property proposer

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and he or she will create the properties,

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and later, you start translating
these properties.

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So let us look at why this matters,
why it is important.

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So I put some examples.

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This is, again, on P31,

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instance of the very, very famous
property P31,

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and you see there is
no description for this item.

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There are almost
six descriptions on this image,

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where we do not have any description.

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Again, some more description
for Odia and Punjabi,

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there is no description.

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This is a property
which is used quite a lot,

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and you see that there is
no description for it.

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And there is a surprising part
that you could also have cases

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where there are descriptions,
but there are no labels.

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For example, Ruffian,
that has been shown here,

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again on property P31,
there is a label that is missing.

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So this was the initial
inspiration for this work

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when I started working
on property analysis.

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I wanted to look at
what aspects of properties,

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or what aspects of property

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that the whole flow chart
that we have seen,

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is multilingual.

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So I wanted to look at,

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okay, we know that Wikidata
is multilingual,

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and it's collaborative,
that has been done.

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But are we really able to achieve
a truly multilingual experience?

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That was the question
behind the creation of WDProp.

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So you may ask
why there are so many people

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who have worked on items,
there are people who have worked on--

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users, multilingual users
and bots, et cetera,

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why you want to focus on properties?

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The answer is,
I want to focus on properties

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because it's very, very
less influenced by bots.

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You may have heard today or yesterday,

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many people said,
"Okay, if you have translation

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in your local languages,
and it has reached a very good number,

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you should ensure
what type of translation it is.

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Is it just bots, which copies
the name of a person to another language.

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Then is it really translation?"

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Okay, that's debatable.

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But, of course,
there is an influence by bot,

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but in case of properties,
there is not so much influence by bots,

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and that is a good part.

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That's why I focus on the bots part.

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So, as I said, when WDProp was created,

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it was to understand every aspect--
the proposal, the creation, translation.

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What are the templates that are available.

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Are these templates,
for example, you said support,

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if a French person opens Wikidata,
a Wikidata France translation page,

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can he see the word, [<i>soutien</i>],
for that particular property proposal?

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Is it possible?

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So this type of things was needed.

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In the end, it was also
about giving real-time statistics

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to the multilingual contributors.

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It's not about one time,

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it's like you just made it
and published for one time-- no.

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You want people
to get this data in real time.

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So what are we doing?

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So the goal of WDProp
was to understand everything

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about Wikidata properties.

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So, label, aliases, description.

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So you have got all these three translated
so the middle part where you say,

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this property is completely usable
because all the three aspects

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have been translated.

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So let me just show you quickly,
what is this WDProp,

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what I'm talking about.

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So this is the WDProp,

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it's available on
<i>tools.wmflabs.org/wdprop/.</i>

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So you have a lot statistics
and if I ask you some questions today,

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like, for example,
"How many data types are there

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that are supported by Wikidata right now?"

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So if such questions, we do not know,

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sometimes because there are new data types
that keep on coming.

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So this data,
this is generated at real time,

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this creates the data structure
and it will give you the answer.

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How many languages are there?

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Yes, of course,
see that there are 313 languages.

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And then, for example,
how many labels were translated.

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So you could see
that the data is being fetched.

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I hope it comes.

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Okay, let's hope. (chuckles)

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Okay, I will take
some other stuff as well.

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Browsing all properties by their time.

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Yes. So you see,
this is count of translated labels,

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and you see all this data
that is coming real time,

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and you can see that the labels

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are currently available
in 6,804 languages in English,

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followed by Dutch, followed by Arabic,
followed by Ukrainian, and then French.

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So this is real-time statistics.

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So you could also do the same
for description,

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also do for aliases, et cetera.

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And you could get the overall
translation statuses if you want.

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So there are some other things
that we will discuss later,

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if time permits.

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But you could navigate
all the different items

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on the left-hand side,

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and you could see
there are a lot of things

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that could really help to see
what things are happening in WDProp.

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So this is, for example,
Wikidata properties,

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these are the properties
that are currently available.

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But as I said some time back,
properties could be deleted.

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And this, you see that these are
the properties that were deleted,

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starting from P1, P2, P3, P4, P5,
these have all been deleted,

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and you could get this thing
just from the statistics board.

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And here, so same thing.

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Then, the next thing that interested me
was to understand the translation pattern.

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So, for example, sometimes we feel
that some languages--

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so English is created first,
and followed by maybe Dutch,

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or maybe French,

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and maybe after French,
it could be Arabic.

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So these things
could be interesting to know.

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So for that, we started to look
at the idea of translation path--

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exactly how things are translated.

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So again, if you go to the property page,
you could click on any property.

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Sorry.

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Maybe I can show.

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So you could click on any property
and you could just say,

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"Give me the translation path."

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It takes some time,
but it will start bringing the data,

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because it's real time,
so you get the data coming from all this.

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So you get the date,

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you get what things have been changed,
when was something deleted, et cetera.

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Why it is important?

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For example, you see
this is something that happened in 2017,

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and the label has been removed.

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This is the official website.

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So imagine you have removed the label
from the official website--

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sorry, this country--

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so anybody who doesn't know P17,
what it is, cannot even understand,

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because the label has been deleted
by the person.

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So this type of vandalism exists.

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Another example where, completely,

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all the language labels
have been deleted--

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English, French, Spanish, German,
everything has been deleted.

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There are no labels,
there are no descriptions.

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So you could find these types of things
from the translation path

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and just because of the color code,
you could see what happened on what day,

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and you could check exactly,
because it is also linked.

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00:13:09,666 --> 00:13:14,261
If you click on any of this,
you could also get a link to the revision,

241
00:13:14,261 --> 00:13:19,478
identify what exactly happened
during that particular revision.

242
00:13:19,478 --> 00:13:21,309
So this is coming from revision history.

243
00:13:21,309 --> 00:13:25,311
So if you click on any of this,
you get what exactly is happening

244
00:13:25,311 --> 00:13:28,567
in any particular revision.

245
00:13:28,567 --> 00:13:30,733
So how did we build it?

246
00:13:30,733 --> 00:13:31,923
Just if you come back,

247
00:13:31,923 --> 00:13:38,396
here, you see there is something
called a comment on the right-hand side.

248
00:13:38,396 --> 00:13:42,602
You see there is something
called added aliases,

249
00:13:42,602 --> 00:13:46,613
"added British English aliases,"
"changed Esperanto label,"

250
00:13:46,613 --> 00:13:48,109
"added [io] label," et cetera.

251
00:13:48,109 --> 00:13:50,710
So we made use of this information,

252
00:13:50,710 --> 00:13:53,209
for example,
for label description and aliases,

253
00:13:53,209 --> 00:13:55,507
if you add something,
you have some sort of comment

254
00:13:55,507 --> 00:13:58,216
which starts with <i>wbsetlabel-add.</i>

255
00:13:58,216 --> 00:14:01,635
Or if it is updated,
you have <i>wbsetlabel-set.</i>

256
00:14:01,635 --> 00:14:04,487
And if you remove something,
you see it is removed.

257
00:14:04,487 --> 00:14:06,795
And based on this type of information,

258
00:14:06,795 --> 00:14:11,167
we were able to build
such a translation path.

259
00:14:11,167 --> 00:14:16,557
Okay, this is good, but what happened
is that this type of information,

260
00:14:16,557 --> 00:14:19,366
this type of things,
just using the comment,

261
00:14:19,366 --> 00:14:23,932
it is useful for building real-time tools,
just like what I showed before, WDProp,

262
00:14:23,932 --> 00:14:30,886
but it is very difficult to detect
when there are multiple changes.

263
00:14:30,886 --> 00:14:34,871
For example, if you have seen
bots activity on Wikidata,

264
00:14:34,871 --> 00:14:39,550
some bots make multiple labels
in one single edit.

265
00:14:39,550 --> 00:14:42,037
In that case,
you cannot find what happened

266
00:14:42,037 --> 00:14:45,878
because you do not have <i>wbsetlabel,</i>
that particular language.

267
00:14:45,878 --> 00:14:49,254
So you do not have a set of languages
along with your comment.

268
00:14:49,254 --> 00:14:53,703
So these are some problems
if you want to use this type of approach.

269
00:14:54,603 --> 00:14:58,245
So what we did,
we decided to collect the data,

270
00:14:58,245 --> 00:15:01,316
and we decided to publicly
make this data available.

271
00:15:02,516 --> 00:15:06,246
And what we did,
we wanted to make use of content.

272
00:15:06,246 --> 00:15:08,579
So what we did,
we started with every revision,

273
00:15:08,579 --> 00:15:12,096
and we took the content of each revision.

274
00:15:12,096 --> 00:15:16,717
And we took the next revision,
and we decided to find the difference

275
00:15:16,717 --> 00:15:19,885
between these two revisions,
to find what exactly changes,

276
00:15:19,885 --> 00:15:21,822
which of the labels got changed.

277
00:15:21,822 --> 00:15:25,436
Because of that, we got
much more interesting information,

278
00:15:25,436 --> 00:15:28,899
much more accurate information
than the previous approach

279
00:15:28,899 --> 00:15:31,274
because it is very important
for doing analysis.

280
00:15:31,274 --> 00:15:34,020
It is important
that you make use of correct data.

281
00:15:34,020 --> 00:15:36,866
So you have four columns
that were used here--

282
00:15:36,866 --> 00:15:39,091
timestamp, property,
language, type, et cetera.

283
00:15:39,091 --> 00:15:44,494
And you get this data in this format.
It is publicly available.

284
00:15:44,494 --> 00:15:47,446
So what does this data give me?

285
00:15:47,446 --> 00:15:48,791
This data gives me information

286
00:15:48,791 --> 00:15:54,791
that currently almost 4,000 plus,

287
00:15:54,791 --> 00:15:57,291
4,500 properties

288
00:15:57,291 --> 00:15:59,917
have labels between 0 and 20.

289
00:15:59,917 --> 00:16:02,145
So there are a lot of properties

290
00:16:02,145 --> 00:16:07,107
who do not have
more than 20 multilingual labels.

291
00:16:07,107 --> 00:16:10,888
And there are only
1,500 language properties

292
00:16:10,888 --> 00:16:12,857
that have been translated up to 40.

293
00:16:12,857 --> 00:16:18,699
And yesterday, if you were present
during the talk of Lydia Pintscher,

294
00:16:18,699 --> 00:16:21,967
she talked about P18,
so P18 is something here.

295
00:16:21,967 --> 00:16:25,332
So you can see there are only
a couple of six or seven properties

296
00:16:25,332 --> 00:16:30,147
that are currently having all the--

297
00:16:30,147 --> 00:16:35,092
P18 has 154 translations,
just to give that idea.

298
00:16:35,092 --> 00:16:39,913
So there is one property
which is having 154 multilingual labels.

299
00:16:39,913 --> 00:16:43,807
There are properties
which have only one particular label.

300
00:16:43,807 --> 00:16:50,112
And the average number
of labels is only 21,

301
00:16:50,112 --> 00:16:52,945
and the standard deviation is 20.

302
00:16:52,945 --> 00:16:55,967
Okay, what next we would like to say?

303
00:16:55,967 --> 00:16:59,970
So you have seen something similar
in the real-time data.

304
00:16:59,970 --> 00:17:02,079
This is from the collected data.

305
00:17:02,079 --> 00:17:07,503
So this is what are the top languages
that are coming up in the results.

306
00:17:07,503 --> 00:17:09,186
So these we have seen.

307
00:17:09,186 --> 00:17:13,314
But my next point is,
are there combinations possible.

308
00:17:13,314 --> 00:17:16,522
For example, if there is French,
there is Arabic.

309
00:17:16,522 --> 00:17:19,505
If there is Arabic,
there is some other language.

310
00:17:19,505 --> 00:17:22,102
If there's French,
there's Ukrainian, et cetera.

311
00:17:22,102 --> 00:17:26,093
Can we find such type of combinations
in the translation data set?

312
00:17:26,093 --> 00:17:27,415
So, yes, it is possible.

313
00:17:27,415 --> 00:17:30,195
So if you see this count,
this frequent itemsets--

314
00:17:30,195 --> 00:17:32,134
so I've just shown seven of them--

315
00:17:32,134 --> 00:17:35,315
you find that there are combinations
that are possible.

316
00:17:36,901 --> 00:17:41,397
Okay, let us say, is there a possibility
of having four labels,

317
00:17:41,397 --> 00:17:44,313
like if there is English,
there's also possibility to find Dutch,

318
00:17:44,313 --> 00:17:45,794
Arabic, Ukrainian.

319
00:17:45,794 --> 00:17:48,041
If there is English,
there's possibility to find Dutch,

320
00:17:48,041 --> 00:17:49,798
French, and Arabic, et cetera.

321
00:17:49,798 --> 00:17:52,763
You can also find a lot of combinations.

322
00:17:52,763 --> 00:17:53,907
Why it is important?

323
00:17:53,907 --> 00:17:57,432
Because it is important to know if,

324
00:17:57,432 --> 00:17:59,998
for example,
if you have multilingual speakers

325
00:17:59,998 --> 00:18:03,664
who are contributors,
who can speak multiple languages,

326
00:18:03,664 --> 00:18:07,402
if you're able to find
any particular pattern

327
00:18:07,402 --> 00:18:12,556
that helps us to find
that if you tell this person to translate,

328
00:18:12,556 --> 00:18:15,276
a new property is created
to translate this label,

329
00:18:15,276 --> 00:18:19,213
because he already
speaks multiple languages,

330
00:18:19,213 --> 00:18:21,669
we can suggest these things to the user.

331
00:18:21,669 --> 00:18:24,858
So let's just show you one example.

332
00:18:24,858 --> 00:18:27,257
This is a complete translation path

333
00:18:27,257 --> 00:18:29,774
that has obtained
from different languages.

334
00:18:29,774 --> 00:18:35,001
So here, what we have done is
we selected two small minority languages,

335
00:18:35,001 --> 00:18:39,293
like Tagalog and Kapampangan,

336
00:18:39,293 --> 00:18:42,602
which are minority languages
from Philippines,

337
00:18:42,602 --> 00:18:46,156
and you see that there is
a strong transfer

338
00:18:46,156 --> 00:18:49,645
between Tagalog and Kapampangan.

339
00:18:49,645 --> 00:18:51,784
So these types of things can be detected

340
00:18:51,784 --> 00:18:54,738
when you have such type
of translation results.

341
00:18:54,738 --> 00:18:57,311
So that is another advantage.

342
00:18:57,311 --> 00:18:59,780
To conclude my work,
I would like to say,

343
00:18:59,780 --> 00:19:05,128
this is important that we understand
how properties are translated

344
00:19:05,128 --> 00:19:10,534
because if you want to extract data
from Wikipedia,

345
00:19:10,534 --> 00:19:14,661
you need to know what are the words

346
00:19:14,661 --> 00:19:16,491
in the local languages
that are being used.

347
00:19:16,491 --> 00:19:20,208
What is "image" in French,
what is "image" in Punjabi,

348
00:19:20,208 --> 00:19:22,539
what is "image" in Hindi,
or any other language.

349
00:19:22,539 --> 00:19:25,890
So that is important for importing data.

350
00:19:25,890 --> 00:19:30,023
And tomorrow, of course,
if you are able to fetch this data,

351
00:19:30,023 --> 00:19:35,193
to Wikidata, we could also
use new projects like Wikidata Bridge,

352
00:19:35,193 --> 00:19:38,963
which we could use
to fill other info boxes,

353
00:19:38,963 --> 00:19:44,563
like multilingual Wikipedia articles,

354
00:19:44,563 --> 00:19:47,370
and this could be really helpful.

355
00:19:47,370 --> 00:19:51,238
So withe that, I would like to thank you,
and if you have questions,

356
00:19:51,238 --> 00:19:54,321
I would be happy to answer them.

357
00:19:55,131 --> 00:19:57,218
(moderator) Anybody with questions?

358
00:19:58,842 --> 00:20:01,854
(audience applause)

359
00:20:08,387 --> 00:20:09,479
Yes?

360
00:20:11,988 --> 00:20:15,746
(man) So what you're doing
is mainly analyzing how this--

361
00:20:15,746 --> 00:20:17,389
- (John) Yes.
- (man) ...is all happening?

362
00:20:17,389 --> 00:20:21,418
Do you know if there are initiatives
or if there are tools

363
00:20:21,418 --> 00:20:25,331
which can help make this easier,
like translation of properties?

364
00:20:25,331 --> 00:20:28,321
Yes. Tools, like, for example,
what to translate

365
00:20:28,321 --> 00:20:32,995
from Wikimedia Foundation, is helpful,
but I have not seen--

366
00:20:32,995 --> 00:20:35,522
This is not currently
integrated with Wikidata.

367
00:20:35,522 --> 00:20:41,672
What to translate is only integrated
with certain languages on Wikipedia,

368
00:20:41,672 --> 00:20:44,485
but not on Wikidata.

369
00:20:44,485 --> 00:20:46,460
But that could be really interesting.

370
00:20:46,460 --> 00:20:50,165
Yes, thank you for bringing this up,
because just imagine,

371
00:20:50,165 --> 00:20:54,490
if we know that a person
has been labeling in multiple languages,

372
00:20:54,490 --> 00:20:56,842
and we also have
this what to translate tool,

373
00:20:56,842 --> 00:21:00,007
and we have these statistics,
we have this data

374
00:21:00,007 --> 00:21:04,657
coming from this type
of property translation,

375
00:21:04,657 --> 00:21:09,423
it is easier to suggest to a person
that new properties have been created,

376
00:21:09,423 --> 00:21:11,461
and then you could--

377
00:21:11,461 --> 00:21:13,980
Right now it's not integrated to Wikidata.

378
00:21:15,674 --> 00:21:17,432
(moderator) Anybody else?

379
00:21:20,246 --> 00:21:23,315
(man 2) I have one question myself,
that comes back to it,

380
00:21:23,315 --> 00:21:27,748
does anybody know of working lists
on translating properties?

381
00:21:27,748 --> 00:21:28,769
Sorry?

382
00:21:28,769 --> 00:21:30,489
(man 2) Does anybody
know of working lists

383
00:21:30,489 --> 00:21:31,695
about translating properties,

384
00:21:31,695 --> 00:21:37,751
like, I can imagine from your statistics,
you could say, this is the top 100

385
00:21:37,751 --> 00:21:39,944
most widely used properties

386
00:21:39,944 --> 00:21:42,844
who lack translations
in this and this language?

387
00:21:42,844 --> 00:21:47,494
No, there is, I think,
there are ways by,

388
00:21:47,494 --> 00:21:51,112
for example,
you could browse by data types,

389
00:21:51,112 --> 00:21:53,843
browse by property classes.

390
00:21:53,843 --> 00:21:57,398
For example, here is something
called property classes

391
00:21:57,398 --> 00:22:00,743
where people have created projects--

392
00:22:00,743 --> 00:22:03,272
it's taking time--
so you have projects,

393
00:22:03,272 --> 00:22:08,597
and you could say, how would I describe,
what are the, for example,

394
00:22:08,597 --> 00:22:11,978
what are the properties
that I could describe for this,

395
00:22:11,978 --> 00:22:14,183
for describing IEEE standard version?

396
00:22:14,183 --> 00:22:16,846
You need edition number,
you need edition translation, et cetera.

397
00:22:16,846 --> 00:22:22,890
So if you have a targeted thing,
you could search for what type of classes.

398
00:22:22,890 --> 00:22:25,853
For example, if you're working
in GLAM or histories,

399
00:22:25,853 --> 00:22:29,652
you could say, what is history-related
any document are there?

400
00:22:29,652 --> 00:22:32,715
So you could say, historical,
and you could find historical.

401
00:22:32,715 --> 00:22:36,247
Okay, this is a property class,
go to this property class.

402
00:22:36,247 --> 00:22:37,855
And, sorry, where is it?

403
00:22:37,855 --> 00:22:40,437
So it is having something
called "Merimee ID."

404
00:22:40,437 --> 00:22:44,467
So people have been
trying to use property classes

405
00:22:44,467 --> 00:22:45,913
to link objects.

406
00:22:45,913 --> 00:22:49,577
That helps if you're working
on a particular project,

407
00:22:49,577 --> 00:22:52,342
and you could find
that property's related to that.

408
00:22:52,342 --> 00:22:58,246
(man 2) But your tool could quite easily
make a list of, let's say,

409
00:22:58,246 --> 00:23:02,746
the top 100 most widely used properties

410
00:23:02,746 --> 00:23:07,488
who haven't got, I don't know,
Punjabi label, let's say?

411
00:23:07,488 --> 00:23:10,284
- (John) For that, I will just--
- (man 2) Which could be interesting.

412
00:23:10,284 --> 00:23:14,310
(John) Okay, tell me any language,
for example, let us say, Netherlands,

413
00:23:14,310 --> 00:23:17,456
because it's performing very well.

414
00:23:17,456 --> 00:23:21,861
So I would say-- translated labels.

415
00:23:21,861 --> 00:23:24,011
So this is translate-- sorry.

416
00:23:30,491 --> 00:23:33,059
(mouse clicking)

417
00:23:36,747 --> 00:23:38,697
For example, Hindi.

418
00:23:38,697 --> 00:23:40,497
So here, what happens,

419
00:23:40,497 --> 00:23:44,335
here you just see any properties
that need translation.

420
00:23:44,335 --> 00:23:47,473
So there are like 6,647 properties

421
00:23:47,473 --> 00:23:50,299
that need translation
in a particular language.

422
00:23:50,299 --> 00:23:54,998
So you could click on any language
that you want and get the data.

423
00:23:54,998 --> 00:23:58,778
And you could get the list
of where people need support.

424
00:23:58,778 --> 00:24:03,345
So, this could be interesting
to link with property usage,

425
00:24:03,345 --> 00:24:06,232
how many people, is it really top,
is it under the top ten.

426
00:24:06,232 --> 00:24:08,871
So suggest those ten top hundred,
in that language.

427
00:24:08,871 --> 00:24:11,282
That would be an interesting list.
That's good.

428
00:24:11,852 --> 00:24:13,054
(man 3) Just what you asked,

429
00:24:13,054 --> 00:24:17,077
there is a list of top 100
most used properties on Wikidata.

430
00:24:17,077 --> 00:24:18,924
It's on Wikidata.

431
00:24:18,924 --> 00:24:21,432
So, yeah, it's there,

432
00:24:21,432 --> 00:24:25,942
under Wikidata Database Reports/
Top 100 Properties.

433
00:24:25,942 --> 00:24:31,083
So one thing could be that
we could just link this and suggest it.

434
00:24:31,083 --> 00:24:33,349
(moderator) Could you maybe
add the link to the etherpad,

435
00:24:33,349 --> 00:24:37,270
and then maybe,
this information can come together.

436
00:24:37,270 --> 00:24:38,631
(John) Okay.

437
00:24:40,049 --> 00:24:42,007
(moderator) If there is
no other questions,

438
00:24:42,007 --> 00:24:44,045
then we will conclude here.

439
00:24:44,045 --> 00:24:49,236
And we have two, three minutes break
until we start with the next speaker.

440
00:24:49,236 --> 00:24:50,864
- Thanks.
- (John) Thank you very much.

441
00:24:50,864 --> 00:24:53,041
(audience applause)