WEBVTT

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Hi, I am Satdeep. I work
with the Foundation in Ben's team.

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Here's my friend from India, Bodhi.

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He's working with the Centre
for Internet and Society,

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but he's here in his volunteer capacity.

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So, we're going to talk about
knowledge gaps and Wikidata today.

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So what are knowledge gaps?

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As the name suggests, it's a gap
in our existent knowledge.

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But in terms of Wikidata,
we're looking at knowledge gaps

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in two different aspects.

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One is, how can Wikidata help us
in filling the knowledge gaps

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in other Wikimedia projects?

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And the second is, how do we fill
the knowledge gaps within Wikidata?

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For the first one,
"Filling knowledge gaps with Wikidata."

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Wikidata is helping in a number of ways

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in filling knowledge gaps
on different Wikimedia projects,

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for example, ArticlePlaceholder,

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or another tool called Scribe
is being built,

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Wikidata Infoboxes, all of them are--

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(audience reacts)

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Yes, there was a session about it
early this morning or in the afternoon.

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And there are also a lot
of different templates

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which use Wikidata.

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And then there are new templates
called [inaudible],

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which along with this here
are used to make lists like these.

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And if you click on one
of the topics on this list,

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you get this draft article.

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There was a presentation about this
in this same room by [inaudible].

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So you get a draft article 
with some sentences

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and the infoboxes from Wikidata.

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But this is not what we're going
to talk about here today.

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We're going to talk about how, in India,

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we first have to fill
the knowledge gaps within Wikidata,

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so then we can do
all these amazing things.

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So there are knowledge gaps
in localization.

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We need to add a lot more labels
in different languages.

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There needs to build local data
about local places, people,

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so that we can do
all those awesome things.

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But the main aspect of there 
is to build community capacity

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to do all that stuff.

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So, that's where we come
to The Indic Case Study,

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which this is all about.

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And how did it all start?

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There is a person
sitting right there, Asaf.

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He is responsible for all this--

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for bringing Wikidata to India.

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So there was the first community 
capacity development training

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with the Tamil community in 2016,
where he introduced Wikidata.

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And then there was like a bunch
of Wikidatans, super users

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who started contributing to Wikidata.

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And then, in 2017, on both our requests,

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he came to India again and did

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(laughs) Wiki-a-Tra--

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it's like Wiki travel in India.

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He did that, he went
to seven different cities,

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seven different communities
at least, in India,

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where he did Wikidata workshops,

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mostly two-days workshops 
in all those places.

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And then, in 2018, again,
an Advanced Wikidata workshop.

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And that has actually helped in building
some sort of Wikidata community

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around India.

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That also got the community engaged,

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and then we started
building WikiProject India,

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and then some other projects
related to that,

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such as WikiProject West Bengal,
Indian Railways, and Kerala,

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which are like some
specifics regions in India

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where the community has been trying
to engage themselves

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and doing some work around it.

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And then there have been
some more initiatives to engage newbies

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such as edit-a-thons,
or labelathons, datathons,

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with which we've been trying
to get more and more people involved.

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And some initiatives around education,

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workshops in education institutions--
Asaf also did one of those.

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Yeah. Next, Bodhi.

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So, there have been
so many workshops in India,

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throughout all of India
from 2017 to 2019.

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And we're also trying to engage,
as Satdeep said,

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we are trying to engage
the newbies in different ways.

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But still, the number of power users 
are not very much in India.

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Only very few, maybe five or six people
are doing the heavy-duty work.

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So one of the reasons for that:

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mostly the Wikimedia community
is focused in India on other projects,

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mostly in Wikipedia and somehow,
right now, in Wikisource.

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So, there are very few editors who are--

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very few active editors

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who are contributing
to Wikidata regularly.

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India is a multilingual country,

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so there are around
22 Wikimedia projects

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running in India.

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So the workforces are totally divided.

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So, we don't have
a focused group of people

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who are working
on specific areas of Wikidata

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because they are so much divided
into different projects,

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that we have to engage--
we're trying to actively engage them

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in different ways.

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And they are spread over a vast region,

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India is the seventh
largest country in the world,

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and so it's quite difficult to coordinate
the intercommunity,

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the 22 languages communities
to work on only one project.

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So, we have adopted a different approach.

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Firstly, we're targeting the data gaps,

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which is easy because
there are huge data gaps in India

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on every topic, almost every topic.

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And...

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(chuckles)

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...start locally.

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Sorry. (laughs)

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- So, it's 1, 1, 1--
- Everything is a priority!

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(laughter)

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Anyway. So we start locally.

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So we have thought that intercountry--

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the data ingestion
of intercountries is quite difficult.

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And there are huge databases for India,

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for example, the science databases,
the election databases.

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And if we work on the intercountry,

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then it'd be really impossible
for five or six heavy-duty users.

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So we target one place at a time.

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So that is the map of India,

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and you can see the bright pink color
that is West Bengal.

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So in October 2018 to May 2019,
many things happened there.

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So lots of data
were ingested in that part.

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And after this map was generated,

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there is a tool for that
called Wikidata Analysis--

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built by [inaudible], user: [inaudible].

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And after we got this map,

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we shared this with other communities.

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That "We have done this for West Bengal,
you can do it for your country.

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And this is really cool."

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And people have started working--

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that was a direct effect.

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WikiProject Kerala was built
just at that time,

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and they started working
on the schools of India--

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schools of Kerala--
and Kerala is situated right here--

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and I couldn't [locate]
that in the map right now

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because the tool is right now down.

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So we just started locally.

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We're trying to inspire people
from other parts of the country

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to contribute.

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And that's what happened in West Bengal,

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around 40,000 villages
with 2001 and 2011 census.

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Our data was ingested--
that's complete data.

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Almost complete data

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which could have been
ingested in Wikidata.

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And there were 11,000
government hospitals with coordinates

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which were ingested,

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and there was [inaudible] approach
to close to 1 million Bengali labels.

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And so on.

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There were many things happening,
but these were the things

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which we've done
in West Bengal at that time.

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So we also tried
to create cool visualizations

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from those works we've done

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because census and elections,
these are boring data.

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These are not paintings,
and also so we cannot--

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like these are also not GLAM data
and other things.

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So these are boring data.

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So we need to find some way
to make it interesting for people.

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So, we have tried some cool queries.

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This is one of them.
There are many others.

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So this is the population growth
in West Bengal

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between our villages--
around 36,000 villages

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between 2001 and 2011.

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And not only villages,
we have uploaded census data

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about every administrative hierarchy,

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like community developing blocks,
districts, municipalities, wards, etc.,

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cities, towns.

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This is a new tool, InteGraality,

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and you can see

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that this is a count of hospitals

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in the world,

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and India is right now
leading in Wikidata--

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13,466 hospitals.

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The blue colors are the data completeness.

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But the funny thing is--
it's only one area of India.

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It's West Bengal,

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there are 11,642 hospitals right now.

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So if we complete all these steps
and there are more--

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if we complete all those steps,

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there will be a huge amount
of data about hospitals

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with coordinates
which will be there in Wikidata,

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and we have a plan to build an app
based on that data,

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so that when a person gets ill,

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using that app, he may find

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the nearest location of the hospitals.

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So these hospitals are ranging
from Primary Health Centers

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to [inaudible] Health Cares,

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with all sorts of facilities
available for them.

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So we've tried to ingest
all those data in Wikidata,

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if possible.

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And after completing this task, 
if we build some app,

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then maybe someone,
a sick person in a dying urgency

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can find the nearest government hospital.

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- This is another--
- (Satdeep) Go back.

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(Bodhi) Oh, sorry.

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Okay. So this is the work
which was done for Indian Railways.

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It was started there,
also from West Bengal.

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And you can check the color--

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the blue color is more complete data

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and the green color
is slightly not complete,

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but it's going to get completed soon.

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And there are right now,
9,000 Indian railway stations

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with coordinates, obviously,
because they are on the map.

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Right now, they're being connected
with Pakistan and Bangladesh railways.

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So we have a plan to connect
all Asian railways one day--

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someday, maybe.

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(laughs)

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But, yeah, we'll do it.

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Anyway. So, right now on the table,

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we are in the second position
after Japan, obviously.

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And-- yeah. So this is another cool query.

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Visualization showing
the flight connections--

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international and domestic
flight connections from India,

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to and from India.

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So it's like kind of messy,
but we can filter it

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for domestic connections
or international connections.

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So, anyway.

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We have also completed

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everything about 2014
Indian General Election data.

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India general election is a kind 
of complex state of data

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because there are
so many political parties,

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so many election--
not like a two party elections.

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So there were 6,000 political parties 
which participate in Indian--

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I think 600 or something.

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So, anyway.

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So, yeah.

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And there were so many candidates,
you can imagine.

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And some of them have the same name.

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Like in one constituency,

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there was like three people
with the same name.

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(laughs)

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So that was like a funny thing.

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But we completed those data--
uploading those data in Wikidata.

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Right now, only 24 Indian
general elections have been done.

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We don't have much users in Wikidata--
heavy-duty users in Wikidata in India.

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So currently we're uploading
geoshape files of the constituencies.

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In West Bengal, we have
already uploaded 43 constituencies,

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geoshape files of the constituencies,
and also the [inaudible].

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There is another part of India
that has not been done,

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so when it will be completed then--

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when it'll be--

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when we upload other election that are--

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like 2009 or before that,

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we'll create cool animations.

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That's showing how the voters
have changed their minds

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from like centrist to rightist
or leftist to rightist, anyway.

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So in the pipeline, there are schools,

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bank branches, post offices, geoshapes, 
elections, and many more.

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- (man 1) Cinema.
- Cinema, yeah.

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(laughs)

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- Of course, cinema.
- And monuments.

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And monuments.

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And most of them will be completed
within a few months.

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And in a not so distant future,
we'll try to upload weather data.

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There are not much good property
for weather, right now, in Wikidata,

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that's why we're not
touching it right now,

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but we'll do it.

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Also bibliographical data

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for Indian literature data
are also very less in Wikidata.

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And there will be
some institutional partnerships.

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There were some primary talks already,

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and maybe we'll have
some good news in the future.

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So other ways to engage.

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We have created
some subpages of WikiProject India.

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We have created a skillshare initiative--
started a skillshare initiative

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where people who have
slightly more knowledge in Wikidata

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can share something with other people,

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on a one-to-one basis
approaching online or offline way.

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We have also started a newsletter,
a quarterly newsletter,

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the first issue has been published
in October [2018],

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and we are showcasing
cool visualizations in social media

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in Facebook and Twiter channels 
of Wikidata India, every day.

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So these are the links.

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You can find them there.

00:16:41.602 --> 00:16:43.502
Thank you so much for the...

00:16:45.483 --> 00:16:48.853
As most of you can already guess,

00:16:49.784 --> 00:16:54.514
Bodhi is from that part of India,
the West Bengal,

00:16:54.514 --> 00:16:56.554
where they've done all that work.

00:16:57.040 --> 00:16:59.100
(laughs)

00:16:59.100 --> 00:17:04.752
So the West Bengali community in India
has been really doing this amazing work,

00:17:04.752 --> 00:17:07.922
and this needs to go
to other parts of India

00:17:07.922 --> 00:17:10.598
which need more capacity development,

00:17:10.598 --> 00:17:15.704
which need more trainings, 
also more coordination in India.

00:17:16.181 --> 00:17:19.591
And, okay, I would like to end this

00:17:19.591 --> 00:17:22.550
with how you can help in identifying
some of the knowledge gaps

00:17:22.550 --> 00:17:24.765
and taking that conversation forward,

00:17:24.765 --> 00:17:26.685
which is not directly
related with this topic.

00:17:26.685 --> 00:17:30.535
But there is a Wiki project,
"Identifying knowledge gaps,"

00:17:30.535 --> 00:17:34.225
you can join that and share your thoughts.

00:17:34.225 --> 00:17:38.859
We are also trying to use--
how can we use property P5008,

00:17:38.859 --> 00:17:42.749
which is on the focus list
for a specific project--

00:17:42.749 --> 00:17:48.149
how we can use that to surface
certain topics for contest

00:17:48.149 --> 00:17:50.129
or other events.

00:17:50.898 --> 00:17:54.543
And in the end, we'd like to thank you.

00:17:55.248 --> 00:18:01.034
Also, we'd like to thank Asaf
and Mahir and Tito

00:18:01.034 --> 00:18:05.941
who are another
two power users of Wikidata.

00:18:05.941 --> 00:18:08.131
We'd like to sincerely thank everyone.

00:18:08.131 --> 00:18:09.731
Thank you so much.

00:18:09.731 --> 00:18:11.621
(applause)

00:18:15.014 --> 00:18:16.490
Questions.

00:18:17.003 --> 00:18:18.860
(woman 1) Mark here says, "Hi."

00:18:18.860 --> 00:18:20.090
(laughs)

00:18:20.090 --> 00:18:23.089
(moderator) So we have only five minutes
for questions and answers.

00:18:28.457 --> 00:18:30.137
There. There's a question there.

00:18:30.137 --> 00:18:31.777
(woman 1) Do I need the microphone?

00:18:33.626 --> 00:18:36.062
(woman 1) Thank you so much
for your presentation.

00:18:36.062 --> 00:18:39.248
Is this census data--
what exactly kind of data is that,

00:18:39.248 --> 00:18:40.498
that you've been ingesting?

00:18:40.498 --> 00:18:42.930
It's not for individuals, is it?

00:18:42.930 --> 00:18:45.733
It's more like populations
and stuff like that?

00:18:46.148 --> 00:18:49.538
It's population data, mainly.
Demographic data.

00:18:49.842 --> 00:18:52.952
(woman 1) Are there any other things
that have been asked in the census?

00:18:56.347 --> 00:18:59.257
(man 1) For village, gender--

00:19:00.921 --> 00:19:04.251
(man 2) I was a little involved with that,
so I remember what the data looks like.

00:19:04.251 --> 00:19:07.603
Per settlement in India, per village town.

00:19:07.603 --> 00:19:11.926
You have the total population,
the masculine versus feminine population,

00:19:12.481 --> 00:19:15.564
the literate versus illiterate population.

00:19:15.564 --> 00:19:18.944
Within that, you have also
a separation by gender,

00:19:18.944 --> 00:19:20.964
so you know how many
illiterate males there are

00:19:20.964 --> 00:19:23.093
versus so many
illiterate females there are.

00:19:23.093 --> 00:19:24.603
It's actually quite detailed.

00:19:24.603 --> 00:19:28.923
There are hundreds and hundreds
of pieces of data per village.

00:19:29.338 --> 00:19:33.228
Only some of them
have been modeled on Wikidata.

00:19:36.937 --> 00:19:39.517
Just, of course,
no individual census data.

00:19:41.990 --> 00:19:43.793
(woman 1) Sometimes countries get weird.

00:19:45.108 --> 00:19:48.628
(woman 2) So I wanted to ask you
about the label ingestion

00:19:48.628 --> 00:19:50.868
or the translations of labels you do.

00:19:51.622 --> 00:19:53.932
How did you do that? Do you use tools?

00:19:53.932 --> 00:19:56.582
How do you get people to add it
in their native language

00:19:56.582 --> 00:19:58.367
and translate the labels.

00:19:59.425 --> 00:20:02.797
So, mostly TABernacle,

00:20:03.638 --> 00:20:06.927
and QuickStatements.

00:20:06.927 --> 00:20:08.297
Those we can use, QuickStatements.

00:20:08.297 --> 00:20:10.497
(woman 2) Alright. Cool.

00:20:10.497 --> 00:20:13.823
But also at the same time,
like using labelathons as an activity

00:20:13.823 --> 00:20:17.530
to engage more and more people 
to do that activity.

00:20:19.757 --> 00:20:21.234
Asaf.

00:20:21.234 --> 00:20:22.286
The hero.

00:20:24.698 --> 00:20:26.238
(Asaf) A note on TABernacle.

00:20:26.238 --> 00:20:29.266
I just want to mention for anyone
who may be not aware,

00:20:30.349 --> 00:20:33.399
all of us here use Wikidata-related tools

00:20:33.399 --> 00:20:36.607
which means all of us
have used tools by Magnus,

00:20:36.607 --> 00:20:38.297
the amazing tool builder.

00:20:38.297 --> 00:20:41.317
I just wanted to point out
that he's here at the conference.

00:20:41.317 --> 00:20:43.067
So if you haven't had a chance yet

00:20:43.067 --> 00:20:47.780
to thank him for his amazing work
that enables so much impact--

00:20:47.780 --> 00:20:48.817
do so today.

00:20:48.817 --> 00:20:51.757
I'm not sure he is into hugs,
but you can just thank him.

00:20:51.757 --> 00:20:53.407
(laughs)

00:21:05.300 --> 00:21:08.455
(man 3) Was the skillshare working?

00:21:08.455 --> 00:21:11.297
What do you do? What are the results?

00:21:11.635 --> 00:21:13.935
So, the response is [still no].

00:21:15.471 --> 00:21:21.766
But, yeah. We have five or six people
have already requested,

00:21:21.766 --> 00:21:23.386
and we have completed those.

00:21:24.404 --> 00:21:25.614
(Satdeep) That's going on--

00:21:25.614 --> 00:21:29.726
Like, we just need to surface
the value of Wikidata.

00:21:29.726 --> 00:21:31.911
I think we haven't
really been able to do that.

00:21:31.911 --> 00:21:34.841
Also, we haven't been able
to connect with other projects

00:21:34.841 --> 00:21:36.532
that they are already doing,

00:21:36.532 --> 00:21:38.602
like, for example,
Wikisource or Wikipedia.

00:21:38.602 --> 00:21:42.402
Like how we need to communicate
that in a better way

00:21:42.402 --> 00:21:45.312
to the larger community
who is contributing.

00:21:45.312 --> 00:21:49.312
It was just like getting up
and creating a Wiki periodical.

00:21:49.312 --> 00:21:52.102
Like how do we involve them
and bring them here.

00:21:52.102 --> 00:21:53.886
That's still a problem.

00:21:54.378 --> 00:21:56.810
And Bodhi is showing the census data.

00:21:56.810 --> 00:21:58.574
Bodhi, can you please explain?

00:22:02.150 --> 00:22:07.213
(Bodhi) So this is population data
from the 2011 census,

00:22:07.213 --> 00:22:12.486
5007 in 2001 in the census data.

00:22:13.126 --> 00:22:14.709
This is one village.

00:22:14.709 --> 00:22:17.824
So there are like 36,000 villages
or 40,000 villages.

00:22:18.520 --> 00:22:21.022
This is the male population,
female population,

00:22:21.829 --> 00:22:23.129
number of households,

00:22:23.129 --> 00:22:28.050
illiterate population with male,
female, population qualifiers,

00:22:29.358 --> 00:22:31.648
literate population
and illiterate populations,

00:22:31.648 --> 00:22:32.798
and so on.

00:22:32.798 --> 00:22:35.702
And this is the census code
for 2001 and 2011.

00:22:40.319 --> 00:22:43.557
(woman 3) Okay. I just want to say
that I loved your presentation,

00:22:43.557 --> 00:22:47.447
and I wanted to talk nearly
about the same thing tomorrow,

00:22:47.447 --> 00:22:51.650
so it'll be great because tomorrow--
I will just [stay] watch from this one,

00:22:51.650 --> 00:22:54.230
so making my life easier.

00:22:55.544 --> 00:22:58.446
What I wanted to do or to talk about--

00:22:58.446 --> 00:23:02.276
but I think the WikiProject
you're starting on Wikidata

00:23:02.276 --> 00:23:03.671
will do that--

00:23:03.671 --> 00:23:07.671
is all to engage people
not working about India directly,

00:23:07.671 --> 00:23:12.607
but like I have tools, names,
but I don't deal with Indian names

00:23:13.139 --> 00:23:17.139
because I am not sure I understand
all there are on them,

00:23:17.139 --> 00:23:19.725
and I don't want to do
something massively wrong,

00:23:19.725 --> 00:23:21.375
so better to be careful.

00:23:21.375 --> 00:23:26.624
But I just need to ask with someone
who understand all the problems,

00:23:26.624 --> 00:23:28.984
and I can add an automated tool

00:23:28.984 --> 00:23:32.811
and deal with thousands
upon thousands of items.

00:23:33.047 --> 00:23:35.727
And I think they are many, many tools

00:23:35.727 --> 00:23:39.687
already doing some automated description
and things like that

00:23:39.687 --> 00:23:46.104
for which we don't actually
need people every day,

00:23:46.104 --> 00:23:50.918
we just need like 10 minutes time
for someone to tell me

00:23:50.918 --> 00:23:54.778
or to say family names in those languages,

00:23:54.778 --> 00:23:57.278
and then it just added to the tool.

00:23:57.278 --> 00:24:01.917
And you probably know
[automated] description tool,

00:24:02.520 --> 00:24:06.750
but if you just ask the people
who are using it massively

00:24:06.750 --> 00:24:08.810
to just add Indian languages,

00:24:08.810 --> 00:24:12.810
then you have all Wikidatans
doing the same work for you,

00:24:12.810 --> 00:24:15.409
and actually, it is a problem.

00:24:16.969 --> 00:24:20.970
I am helping an African community
build up their Wikidata

00:24:20.970 --> 00:24:23.819
in Wikipedia, so it's not
the same problem,

00:24:23.819 --> 00:24:25.737
but nearly the same problem.

00:24:26.317 --> 00:24:29.347
And that's the problem we have

00:24:29.347 --> 00:24:32.257
which is actually bridging the gap

00:24:32.257 --> 00:24:35.119
between the biggest Wikidatans--

00:24:35.813 --> 00:24:39.313
I am doing works in languages
I don't know a word of,

00:24:40.843 --> 00:24:44.526
but it's this kind of adoption system,

00:24:44.526 --> 00:24:48.566
like I need a native speaker to tell me

00:24:48.566 --> 00:24:51.854
what I can do with all the problems
on all the complicated cases.

00:24:51.854 --> 00:24:55.752
And everything
that I can automate, I will automate.

00:24:55.752 --> 00:24:59.752
And it's just an idea,
but do you think it will be like

00:24:59.752 --> 00:25:04.752
a good idea to create not so specific
Wiki knowledge gap on Wikidata,

00:25:04.752 --> 00:25:09.462
but a matching system

00:25:09.462 --> 00:25:15.821
like, "Hey I am working on this subject,
do you want to ask me for that?"

00:25:15.821 --> 00:25:20.521
- Like, yeah, a matching tool, like to--
- Connect people.

00:25:20.521 --> 00:25:22.980
- (woman 3) To connect people
across languages.

00:25:24.119 --> 00:25:26.910
Yeah. So that was my idea
because I think

00:25:27.253 --> 00:25:30.273
some of the African communities
I am helping,

00:25:30.273 --> 00:25:33.177
would really, really love
what you're doing,

00:25:33.177 --> 00:25:39.253
but none of them speak Indian,
and we just need to have pivot people

00:25:39.746 --> 00:25:41.266
to create the link

00:25:41.523 --> 00:25:44.504
and make all this even more powerful.

00:25:44.504 --> 00:25:47.341
And I really, really love
what you're doing. So thank you.

00:25:47.341 --> 00:25:48.551
Thank you so much.

00:25:48.551 --> 00:25:50.619
Thanks to Bodhi for all the awesome work.

00:25:51.341 --> 00:25:52.728
(laughs)

00:25:52.728 --> 00:25:54.498
And the larger Indian community.

00:25:54.498 --> 00:25:57.113
But that's a really good idea,
I think we should take that up.

00:25:57.683 --> 00:26:03.090
As a movement, we have not been doing
the sharing thing pretty good.

00:26:03.090 --> 00:26:04.840
We need to figure out how to do that.

00:26:04.840 --> 00:26:06.727
Because there are awesome tools,

00:26:06.727 --> 00:26:09.028
one is built, but the others
don't know about.

00:26:09.028 --> 00:26:10.864
That's a larger problem,

00:26:10.864 --> 00:26:14.197
and that's a piece that fits
into the larger problem.

00:26:14.197 --> 00:26:16.061
We should be solving someplace.

00:26:16.061 --> 00:26:18.050
Let's figure out where we can do that.

00:26:19.421 --> 00:26:20.867
Thank you.

00:26:20.867 --> 00:26:23.027
(applause)