0:00:05.929,0:00:09.349
Hi, I am Satdeep. I work[br]with the Foundation in Ben's team.

0:00:10.315,0:00:12.851
Here's my friend from India, Bodhi.

0:00:12.851,0:00:15.461
He's working with the Centre[br]for Internet and Society,

0:00:15.461,0:00:18.541
but he's here in his volunteer capacity.

0:00:19.523,0:00:24.769
So, we're going to talk about[br]knowledge gaps and Wikidata today.

0:00:25.340,0:00:27.080
So what are knowledge gaps?

0:00:27.651,0:00:31.651
As the name suggests, it's a gap[br]in our existent knowledge.

0:00:31.651,0:00:36.421
But in terms of Wikidata,[br]we're looking at knowledge gaps

0:00:36.421,0:00:38.261
in two different aspects.

0:00:38.261,0:00:43.101
One is, how can Wikidata help us[br]in filling the knowledge gaps

0:00:43.101,0:00:45.141
in other Wikimedia projects?

0:00:45.141,0:00:50.253
And the second is, how do we fill[br]the knowledge gaps within Wikidata?

0:00:52.287,0:00:57.187
For the first one,[br]"Filling knowledge gaps with Wikidata."

0:00:57.187,0:00:58.887
Wikidata is helping in a number of ways

0:00:58.887,0:01:01.177
in filling knowledge gaps[br]on different Wikimedia projects,

0:01:01.177,0:01:02.767
for example, ArticlePlaceholder,

0:01:02.767,0:01:06.147
or another tool called Scribe[br]is being built,

0:01:06.147,0:01:08.908
Wikidata Infoboxes, all of them are--

0:01:08.908,0:01:09.908
(audience reacts)

0:01:09.908,0:01:14.218
Yes, there was a session about it[br]early this morning or in the afternoon.

0:01:14.713,0:01:18.713
And there are also a lot[br]of different templates

0:01:19.700,0:01:22.080
which use Wikidata.

0:01:22.429,0:01:28.079
And then there are new templates[br]called [inaudible],

0:01:28.773,0:01:34.242
which along with this here[br]are used to make lists like these.

0:01:34.242,0:01:37.342
And if you click on one[br]of the topics on this list,

0:01:37.702,0:01:39.522
you get this draft article.

0:01:39.522,0:01:42.988
There was a presentation about this[br]in this same room by [inaudible].

0:01:42.988,0:01:46.792
So you get a draft article [br]with some sentences

0:01:46.792,0:01:50.152
and the infoboxes from Wikidata.

0:01:51.194,0:01:54.988
But this is not what we're going[br]to talk about here today.

0:01:55.705,0:01:59.275
We're going to talk about how, in India,

0:01:59.275,0:02:03.203
we first have to fill[br]the knowledge gaps within Wikidata,

0:02:03.203,0:02:05.563
so then we can do[br]all these amazing things.

0:02:05.563,0:02:09.223
So there are knowledge gaps[br]in localization.

0:02:09.223,0:02:12.323
We need to add a lot more labels[br]in different languages.

0:02:13.256,0:02:16.306
There needs to build local data[br]about local places, people,

0:02:16.306,0:02:18.576
so that we can do[br]all those awesome things.

0:02:18.576,0:02:23.188
But the main aspect of there [br]is to build community capacity

0:02:23.188,0:02:24.858
to do all that stuff.

0:02:24.858,0:02:28.888
So, that's where we come[br]to The Indic Case Study,

0:02:28.888,0:02:30.648
which this is all about.

0:02:30.648,0:02:33.238
And how did it all start?

0:02:33.618,0:02:37.133
There is a person[br]sitting right there, Asaf.

0:02:37.133,0:02:39.610
He is responsible for all this--

0:02:40.463,0:02:42.826
for bringing Wikidata to India.

0:02:43.528,0:02:48.127
So there was the first community [br]capacity development training

0:02:49.376,0:02:53.086
with the Tamil community in 2016,[br]where he introduced Wikidata.

0:02:53.086,0:02:58.117
And then there was like a bunch[br]of Wikidatans, super users

0:02:58.117,0:03:00.447
who started contributing to Wikidata.

0:03:00.447,0:03:04.970
And then, in 2017, on both our requests,

0:03:04.970,0:03:07.940
he came to India again and did

0:03:07.940,0:03:09.697
(laughs) Wiki-a-Tra--

0:03:09.697,0:03:12.709
it's like Wiki travel in India.

0:03:12.709,0:03:16.366
He did that, he went[br]to seven different cities,

0:03:16.366,0:03:19.419
seven different communities[br]at least, in India,

0:03:19.419,0:03:22.772
where he did Wikidata workshops,

0:03:23.192,0:03:26.052
mostly two-days workshops [br]in all those places.

0:03:26.052,0:03:30.277
And then, in 2018, again,[br]an Advanced Wikidata workshop.

0:03:30.277,0:03:34.737
And that has actually helped in building[br]some sort of Wikidata community

0:03:34.737,0:03:36.709
around India.

0:03:39.848,0:03:42.498
That also got the community engaged,

0:03:42.498,0:03:45.258
and then we started[br]building WikiProject India,

0:03:45.258,0:03:47.328
and then some other projects[br]related to that,

0:03:47.328,0:03:51.468
such as WikiProject West Bengal,[br]Indian Railways, and Kerala,

0:03:51.468,0:03:53.748
which are like some[br]specifics regions in India

0:03:53.748,0:03:56.038
where the community has been trying[br]to engage themselves

0:03:56.038,0:03:58.338
and doing some work around it.

0:03:58.338,0:04:02.868
And then there have been[br]some more initiatives to engage newbies

0:04:02.868,0:04:07.598
such as edit-a-thons,[br]or labelathons, datathons,

0:04:08.655,0:04:11.925
with which we've been trying[br]to get more and more people involved.

0:04:11.925,0:04:14.895
And some initiatives around education,

0:04:14.895,0:04:18.455
workshops in education institutions--[br]Asaf also did one of those.

0:04:20.697,0:04:22.252
Yeah. Next, Bodhi.

0:04:23.042,0:04:26.432
So, there have been[br]so many workshops in India,

0:04:26.432,0:04:30.361
throughout all of India[br]from 2017 to 2019.

0:04:30.361,0:04:33.121
And we're also trying to engage,[br]as Satdeep said,

0:04:33.121,0:04:35.431
we are trying to engage[br]the newbies in different ways.

0:04:35.431,0:04:40.955
But still, the number of power users [br]are not very much in India.

0:04:41.915,0:04:48.166
Only very few, maybe five or six people[br]are doing the heavy-duty work.

0:04:48.975,0:04:51.795
So one of the reasons for that:

0:04:52.891,0:04:58.961
mostly the Wikimedia community[br]is focused in India on other projects,

0:04:58.961,0:05:04.183
mostly in Wikipedia and somehow,[br]right now, in Wikisource.

0:05:04.183,0:05:08.344
So, there are very few editors who are--

0:05:08.344,0:05:09.644
very few active editors

0:05:09.644,0:05:12.344
who are contributing[br]to Wikidata regularly.

0:05:13.264,0:05:15.506
India is a multilingual country,

0:05:15.506,0:05:18.914
so there are around[br]22 Wikimedia projects

0:05:18.914,0:05:20.203
running in India.

0:05:20.203,0:05:22.935
So the workforces are totally divided.

0:05:23.630,0:05:28.530
So, we don't have[br]a focused group of people

0:05:28.530,0:05:31.439
who are working[br]on specific areas of Wikidata

0:05:31.439,0:05:34.540
because they are so much divided[br]into different projects,

0:05:34.540,0:05:39.090
that we have to engage--[br]we're trying to actively engage them

0:05:39.090,0:05:40.442
in different ways.

0:05:40.442,0:05:42.013
And they are spread over a vast region,

0:05:42.013,0:05:44.292
India is the seventh[br]largest country in the world,

0:05:44.292,0:05:48.462
and so it's quite difficult to coordinate[br]the intercommunity,

0:05:48.462,0:05:53.882
the 22 languages communities[br]to work on only one project.

0:05:56.246,0:05:59.898
So, we have adopted a different approach.

0:05:59.898,0:06:02.868
Firstly, we're targeting the data gaps,

0:06:02.868,0:06:06.736
which is easy because[br]there are huge data gaps in India

0:06:07.478,0:06:10.209
on every topic, almost every topic.

0:06:10.209,0:06:11.486
And...

0:06:12.681,0:06:14.356
(chuckles)

0:06:14.356,0:06:15.598
...start locally.

0:06:16.358,0:06:17.770
Sorry. (laughs)

0:06:17.770,0:06:21.568
- So, it's 1, 1, 1--[br]- Everything is a priority!

0:06:21.568,0:06:23.678
(laughter)

0:06:25.368,0:06:28.028
Anyway. So we start locally.

0:06:28.766,0:06:34.666
So we have thought that intercountry--

0:06:34.666,0:06:37.727
the data ingestion[br]of intercountries is quite difficult.

0:06:37.727,0:06:41.087
And there are huge databases for India,

0:06:41.087,0:06:44.596
for example, the science databases,[br]the election databases.

0:06:44.596,0:06:48.916
And if we work on the intercountry,

0:06:48.916,0:06:54.369
then it'd be really impossible[br]for five or six heavy-duty users.

0:06:54.369,0:06:58.009
So we target one place at a time.

0:06:58.009,0:07:01.779
So that is the map of India,

0:07:01.779,0:07:06.315
and you can see the bright pink color[br]that is West Bengal.

0:07:06.315,0:07:12.243
So in October 2018 to May 2019,[br]many things happened there.

0:07:12.243,0:07:17.288
So lots of data[br]were ingested in that part.

0:07:17.288,0:07:20.928
And after this map was generated,

0:07:20.928,0:07:24.184
there is a tool for that[br]called Wikidata Analysis--

0:07:24.184,0:07:26.314
built by [inaudible], user: [inaudible].

0:07:26.314,0:07:32.023
And after we got this map,

0:07:32.973,0:07:36.323
we shared this with other communities.

0:07:36.323,0:07:39.603
That "We have done this for West Bengal,[br]you can do it for your country.

0:07:39.603,0:07:41.043
And this is really cool."

0:07:41.043,0:07:43.983
And people have started working--

0:07:44.855,0:07:46.476
that was a direct effect.

0:07:46.476,0:07:50.656
WikiProject Kerala was built[br]just at that time,

0:07:50.656,0:07:54.656
and they started working[br]on the schools of India--

0:07:54.656,0:07:58.048
schools of Kerala--[br]and Kerala is situated right here--

0:07:58.048,0:08:01.086
and I couldn't [locate][br]that in the map right now

0:08:01.086,0:08:05.796
because the tool is right now down.

0:08:07.316,0:08:10.280
So we just started locally.

0:08:11.245,0:08:14.525
We're trying to inspire people[br]from other parts of the country

0:08:14.525,0:08:15.729
to contribute.

0:08:16.833,0:08:19.962
And that's what happened in West Bengal,

0:08:19.962,0:08:25.431
around 40,000 villages[br]with 2001 and 2011 census.

0:08:25.431,0:08:28.860
Our data was ingested--[br]that's complete data.

0:08:28.860,0:08:30.639
Almost complete data

0:08:30.639,0:08:33.869
which could have been[br]ingested in Wikidata.

0:08:34.435,0:08:38.677
And there were 11,000[br]government hospitals with coordinates

0:08:38.677,0:08:40.317
which were ingested,

0:08:40.317,0:08:45.260
and there was [inaudible] approach[br]to close to 1 million Bengali labels.

0:08:46.191,0:08:47.261
And so on.

0:08:47.261,0:08:50.668
There were many things happening,[br]but these were the things

0:08:50.668,0:08:53.843
which we've done[br]in West Bengal at that time.

0:08:53.843,0:08:58.243
So we also tried[br]to create cool visualizations

0:08:58.243,0:09:00.261
from those works we've done

0:09:00.261,0:09:02.929
because census and elections,[br]these are boring data.

0:09:02.929,0:09:07.429
These are not paintings,[br]and also so we cannot--

0:09:07.429,0:09:11.239
like these are also not GLAM data[br]and other things.

0:09:11.239,0:09:13.029
So these are boring data.

0:09:13.029,0:09:19.460
So we need to find some way[br]to make it interesting for people.

0:09:19.460,0:09:22.790
So, we have tried some cool queries.

0:09:22.790,0:09:24.897
This is one of them.[br]There are many others.

0:09:25.321,0:09:27.423
So this is the population growth[br]in West Bengal

0:09:27.423,0:09:31.651
between our villages--[br]around 36,000 villages

0:09:31.651,0:09:34.037
between 2001 and 2011.

0:09:34.658,0:09:37.778
And not only villages,[br]we have uploaded census data

0:09:37.778,0:09:42.547
about every administrative hierarchy,

0:09:42.547,0:09:48.353
like community developing blocks,[br]districts, municipalities, wards, etc.,

0:09:48.353,0:09:49.575
cities, towns.

0:09:51.669,0:09:56.893
This is a new tool, InteGraality,

0:09:57.722,0:10:00.292
and you can see

0:10:00.292,0:10:05.487
that this is a count of hospitals

0:10:06.970,0:10:08.160
in the world,

0:10:08.160,0:10:11.310
and India is right now[br]leading in Wikidata--

0:10:11.310,0:10:15.500
13,466 hospitals.

0:10:18.168,0:10:21.218
The blue colors are the data completeness.

0:10:22.491,0:10:29.539
But the funny thing is--[br]it's only one area of India.

0:10:29.539,0:10:30.995
It's West Bengal,

0:10:30.995,0:10:33.759
there are 11,642 hospitals right now.

0:10:33.759,0:10:38.168
So if we complete all these steps[br]and there are more--

0:10:40.130,0:10:41.740
if we complete all those steps,

0:10:41.740,0:10:45.280
there will be a huge amount[br]of data about hospitals

0:10:45.280,0:10:48.030
with coordinates[br]which will be there in Wikidata,

0:10:48.030,0:10:54.916
and we have a plan to build an app[br]based on that data,

0:10:55.934,0:10:59.134
so that when a person gets ill,

0:11:02.801,0:11:04.591
using that app, he may find

0:11:04.591,0:11:08.718
the nearest location of the hospitals.

0:11:15.268,0:11:19.268
So these hospitals are ranging[br]from Primary Health Centers

0:11:19.268,0:11:20.966
to [inaudible] Health Cares,

0:11:21.941,0:11:26.881
with all sorts of facilities[br]available for them.

0:11:26.881,0:11:31.191
So we've tried to ingest[br]all those data in Wikidata,

0:11:31.191,0:11:32.541
if possible.

0:11:33.251,0:11:37.390
And after completing this task, [br]if we build some app,

0:11:37.390,0:11:41.689
then maybe someone,[br]a sick person in a dying urgency

0:11:41.689,0:11:45.366
can find the nearest government hospital.

0:11:48.795,0:11:52.255
- This is another--[br]- (Satdeep) Go back.

0:11:52.255,0:11:53.743
(Bodhi) Oh, sorry.

0:11:54.914,0:12:01.573
Okay. So this is the work[br]which was done for Indian Railways.

0:12:01.573,0:12:04.188
It was started there,[br]also from West Bengal.

0:12:04.737,0:12:08.994
And you can check the color--

0:12:08.994,0:12:11.094
the blue color is more complete data

0:12:11.094,0:12:14.818
and the green color[br]is slightly not complete,

0:12:14.818,0:12:18.368
but it's going to get completed soon.

0:12:18.626,0:12:22.863
And there are right now,[br]9,000 Indian railway stations

0:12:22.863,0:12:25.764
with coordinates, obviously,[br]because they are on the map.

0:12:25.764,0:12:29.504
Right now, they're being connected[br]with Pakistan and Bangladesh railways.

0:12:29.504,0:12:34.682
So we have a plan to connect[br]all Asian railways one day--

0:12:34.682,0:12:35.992
someday, maybe.

0:12:35.992,0:12:37.042
(laughs)

0:12:37.042,0:12:38.760
But, yeah, we'll do it.

0:12:39.334,0:12:44.314
Anyway. So, right now on the table,

0:12:44.314,0:12:48.854
we are in the second position[br]after Japan, obviously.

0:12:49.947,0:12:53.477
And-- yeah. So this is another cool query.

0:12:54.012,0:12:59.487
Visualization showing[br]the flight connections--

0:12:59.726,0:13:01.968
international and domestic[br]flight connections from India,

0:13:01.968,0:13:03.146
to and from India.

0:13:03.146,0:13:05.803
So it's like kind of messy,[br]but we can filter it

0:13:05.803,0:13:09.573
for domestic connections[br]or international connections.

0:13:09.573,0:13:11.053
So, anyway.

0:13:13.061,0:13:14.641
We have also completed

0:13:14.641,0:13:18.141
everything about 2014[br]Indian General Election data.

0:13:18.141,0:13:22.914
India general election is a kind [br]of complex state of data

0:13:22.914,0:13:25.314
because there are[br]so many political parties,

0:13:25.314,0:13:27.646
so many election--[br]not like a two party elections.

0:13:28.617,0:13:32.407
So there were 6,000 political parties [br]which participate in Indian--

0:13:33.064,0:13:36.449
I think 600 or something.

0:13:36.449,0:13:38.826
So, anyway.

0:13:38.826,0:13:40.336
So, yeah.

0:13:41.461,0:13:44.531
And there were so many candidates,[br]you can imagine.

0:13:45.239,0:13:48.949
And some of them have the same name.

0:13:49.522,0:13:51.195
Like in one constituency,

0:13:51.195,0:13:53.365
there was like three people[br]with the same name.

0:13:53.365,0:13:54.445
(laughs)

0:13:54.445,0:13:56.633
So that was like a funny thing.

0:13:58.456,0:14:04.213
But we completed those data--[br]uploading those data in Wikidata.

0:14:04.213,0:14:07.501
Right now, only 24 Indian[br]general elections have been done.

0:14:07.501,0:14:13.324
We don't have much users in Wikidata--[br]heavy-duty users in Wikidata in India.

0:14:13.805,0:14:18.624
So currently we're uploading[br]geoshape files of the constituencies.

0:14:19.036,0:14:23.884
In West Bengal, we have[br]already uploaded 43 constituencies,

0:14:23.884,0:14:28.519
geoshape files of the constituencies,[br]and also the [inaudible].

0:14:28.519,0:14:31.539
There is another part of India[br]that has not been done,

0:14:31.539,0:14:33.569
so when it will be completed then--

0:14:33.569,0:14:34.860
when it'll be--

0:14:35.185,0:14:38.861
when we upload other election that are--

0:14:38.861,0:14:42.529
like 2009 or before that,

0:14:42.529,0:14:45.588
we'll create cool animations.

0:14:45.588,0:14:51.190
That's showing how the voters[br]have changed their minds

0:14:51.190,0:14:54.953
from like centrist to rightist[br]or leftist to rightist, anyway.

0:14:54.953,0:14:58.668
So in the pipeline, there are schools,

0:14:58.668,0:15:02.818
bank branches, post offices, geoshapes, [br]elections, and many more.

0:15:04.124,0:15:06.164
- (man 1) Cinema.[br]- Cinema, yeah.

0:15:06.164,0:15:07.224
(laughs)

0:15:07.224,0:15:09.039
- Of course, cinema.[br]- And monuments.

0:15:09.039,0:15:10.529
And monuments.

0:15:11.211,0:15:14.761
And most of them will be completed[br]within a few months.

0:15:16.272,0:15:21.872
And in a not so distant future,[br]we'll try to upload weather data.

0:15:22.974,0:15:27.764
There are not much good property[br]for weather, right now, in Wikidata,

0:15:28.274,0:15:31.110
that's why we're not[br]touching it right now,

0:15:31.110,0:15:32.448
but we'll do it.

0:15:32.862,0:15:35.988
Also bibliographical data

0:15:36.415,0:15:40.215
for Indian literature data[br]are also very less in Wikidata.

0:15:41.485,0:15:46.244
And there will be[br]some institutional partnerships.

0:15:46.746,0:15:48.606
There were some primary talks already,

0:15:48.606,0:15:51.596
and maybe we'll have[br]some good news in the future.

0:15:54.140,0:15:55.550
So other ways to engage.

0:15:55.550,0:16:00.013
We have created[br]some subpages of WikiProject India.

0:16:00.406,0:16:05.256
We have created a skillshare initiative--[br]started a skillshare initiative

0:16:05.256,0:16:08.646
where people who have[br]slightly more knowledge in Wikidata

0:16:08.646,0:16:11.876
can share something with other people,

0:16:11.876,0:16:16.756
on a one-to-one basis[br]approaching online or offline way.

0:16:16.756,0:16:20.073
We have also started a newsletter,[br]a quarterly newsletter,

0:16:20.073,0:16:25.739
the first issue has been published[br]in October [2018],

0:16:26.431,0:16:30.512
and we are showcasing[br]cool visualizations in social media

0:16:30.512,0:16:34.478
in Facebook and Twiter channels [br]of Wikidata India, every day.

0:16:34.990,0:16:38.420
So these are the links.

0:16:38.420,0:16:40.286
You can find them there.

0:16:41.602,0:16:43.502
Thank you so much for the...

0:16:45.483,0:16:48.853
As most of you can already guess,

0:16:49.784,0:16:54.514
Bodhi is from that part of India,[br]the West Bengal,

0:16:54.514,0:16:56.554
where they've done all that work.

0:16:57.040,0:16:59.100
(laughs)

0:16:59.100,0:17:04.752
So the West Bengali community in India[br]has been really doing this amazing work,

0:17:04.752,0:17:07.922
and this needs to go[br]to other parts of India

0:17:07.922,0:17:10.598
which need more capacity development,

0:17:10.598,0:17:15.704
which need more trainings, [br]also more coordination in India.

0:17:16.181,0:17:19.591
And, okay, I would like to end this

0:17:19.591,0:17:22.550
with how you can help in identifying[br]some of the knowledge gaps

0:17:22.550,0:17:24.765
and taking that conversation forward,

0:17:24.765,0:17:26.685
which is not directly[br]related with this topic.

0:17:26.685,0:17:30.535
But there is a Wiki project,[br]"Identifying knowledge gaps,"

0:17:30.535,0:17:34.225
you can join that and share your thoughts.

0:17:34.225,0:17:38.859
We are also trying to use--[br]how can we use property P5008,

0:17:38.859,0:17:42.749
which is on the focus list[br]for a specific project--

0:17:42.749,0:17:48.149
how we can use that to surface[br]certain topics for contest

0:17:48.149,0:17:50.129
or other events.

0:17:50.898,0:17:54.543
And in the end, we'd like to thank you.

0:17:55.248,0:18:01.034
Also, we'd like to thank Asaf[br]and Mahir and Tito

0:18:01.034,0:18:05.941
who are another[br]two power users of Wikidata.

0:18:05.941,0:18:08.131
We'd like to sincerely thank everyone.

0:18:08.131,0:18:09.731
Thank you so much.

0:18:09.731,0:18:11.621
(applause)

0:18:15.014,0:18:16.490
Questions.

0:18:17.003,0:18:18.860
(woman 1) Mark here says, "Hi."

0:18:18.860,0:18:20.090
(laughs)

0:18:20.090,0:18:23.089
(moderator) So we have only five minutes[br]for questions and answers.

0:18:28.457,0:18:30.137
There. There's a question there.

0:18:30.137,0:18:31.777
(woman 1) Do I need the microphone?

0:18:33.626,0:18:36.062
(woman 1) Thank you so much[br]for your presentation.

0:18:36.062,0:18:39.248
Is this census data--[br]what exactly kind of data is that,

0:18:39.248,0:18:40.498
that you've been ingesting?

0:18:40.498,0:18:42.930
It's not for individuals, is it?

0:18:42.930,0:18:45.733
It's more like populations[br]and stuff like that?

0:18:46.148,0:18:49.538
It's population data, mainly.[br]Demographic data.

0:18:49.842,0:18:52.952
(woman 1) Are there any other things[br]that have been asked in the census?

0:18:56.347,0:18:59.257
(man 1) For village, gender--

0:19:00.921,0:19:04.251
(man 2) I was a little involved with that,[br]so I remember what the data looks like.

0:19:04.251,0:19:07.603
Per settlement in India, per village town.

0:19:07.603,0:19:11.926
You have the total population,[br]the masculine versus feminine population,

0:19:12.481,0:19:15.564
the literate versus illiterate population.

0:19:15.564,0:19:18.944
Within that, you have also[br]a separation by gender,

0:19:18.944,0:19:20.964
so you know how many[br]illiterate males there are

0:19:20.964,0:19:23.093
versus so many[br]illiterate females there are.

0:19:23.093,0:19:24.603
It's actually quite detailed.

0:19:24.603,0:19:28.923
There are hundreds and hundreds[br]of pieces of data per village.

0:19:29.338,0:19:33.228
Only some of them[br]have been modeled on Wikidata.

0:19:36.937,0:19:39.517
Just, of course,[br]no individual census data.

0:19:41.990,0:19:43.793
(woman 1) Sometimes countries get weird.

0:19:45.108,0:19:48.628
(woman 2) So I wanted to ask you[br]about the label ingestion

0:19:48.628,0:19:50.868
or the translations of labels you do.

0:19:51.622,0:19:53.932
How did you do that? Do you use tools?

0:19:53.932,0:19:56.582
How do you get people to add it[br]in their native language

0:19:56.582,0:19:58.367
and translate the labels.

0:19:59.425,0:20:02.797
So, mostly TABernacle,

0:20:03.638,0:20:06.927
and QuickStatements.

0:20:06.927,0:20:08.297
Those we can use, QuickStatements.

0:20:08.297,0:20:10.497
(woman 2) Alright. Cool.

0:20:10.497,0:20:13.823
But also at the same time,[br]like using labelathons as an activity

0:20:13.823,0:20:17.530
to engage more and more people [br]to do that activity.

0:20:19.757,0:20:21.234
Asaf.

0:20:21.234,0:20:22.286
The hero.

0:20:24.698,0:20:26.238
(Asaf) A note on TABernacle.

0:20:26.238,0:20:29.266
I just want to mention for anyone[br]who may be not aware,

0:20:30.349,0:20:33.399
all of us here use Wikidata-related tools

0:20:33.399,0:20:36.607
which means all of us[br]have used tools by Magnus,

0:20:36.607,0:20:38.297
the amazing tool builder.

0:20:38.297,0:20:41.317
I just wanted to point out[br]that he's here at the conference.

0:20:41.317,0:20:43.067
So if you haven't had a chance yet

0:20:43.067,0:20:47.780
to thank him for his amazing work[br]that enables so much impact--

0:20:47.780,0:20:48.817
do so today.

0:20:48.817,0:20:51.757
I'm not sure he is into hugs,[br]but you can just thank him.

0:20:51.757,0:20:53.407
(laughs)

0:21:05.300,0:21:08.455
(man 3) Was the skillshare working?

0:21:08.455,0:21:11.297
What do you do? What are the results?

0:21:11.635,0:21:13.935
So, the response is [still no].

0:21:15.471,0:21:21.766
But, yeah. We have five or six people[br]have already requested,

0:21:21.766,0:21:23.386
and we have completed those.

0:21:24.404,0:21:25.614
(Satdeep) That's going on--

0:21:25.614,0:21:29.726
Like, we just need to surface[br]the value of Wikidata.

0:21:29.726,0:21:31.911
I think we haven't[br]really been able to do that.

0:21:31.911,0:21:34.841
Also, we haven't been able[br]to connect with other projects

0:21:34.841,0:21:36.532
that they are already doing,

0:21:36.532,0:21:38.602
like, for example,[br]Wikisource or Wikipedia.

0:21:38.602,0:21:42.402
Like how we need to communicate[br]that in a better way

0:21:42.402,0:21:45.312
to the larger community[br]who is contributing.

0:21:45.312,0:21:49.312
It was just like getting up[br]and creating a Wiki periodical.

0:21:49.312,0:21:52.102
Like how do we involve them[br]and bring them here.

0:21:52.102,0:21:53.886
That's still a problem.

0:21:54.378,0:21:56.810
And Bodhi is showing the census data.

0:21:56.810,0:21:58.574
Bodhi, can you please explain?

0:22:02.150,0:22:07.213
(Bodhi) So this is population data[br]from the 2011 census,

0:22:07.213,0:22:12.486
5007 in 2001 in the census data.

0:22:13.126,0:22:14.709
This is one village.

0:22:14.709,0:22:17.824
So there are like 36,000 villages[br]or 40,000 villages.

0:22:18.520,0:22:21.022
This is the male population,[br]female population,

0:22:21.829,0:22:23.129
number of households,

0:22:23.129,0:22:28.050
illiterate population with male,[br]female, population qualifiers,

0:22:29.358,0:22:31.648
literate population[br]and illiterate populations,

0:22:31.648,0:22:32.798
and so on.

0:22:32.798,0:22:35.702
And this is the census code[br]for 2001 and 2011.

0:22:40.319,0:22:43.557
(woman 3) Okay. I just want to say[br]that I loved your presentation,

0:22:43.557,0:22:47.447
and I wanted to talk nearly[br]about the same thing tomorrow,

0:22:47.447,0:22:51.650
so it'll be great because tomorrow--[br]I will just [stay] watch from this one,

0:22:51.650,0:22:54.230
so making my life easier.

0:22:55.544,0:22:58.446
What I wanted to do or to talk about--

0:22:58.446,0:23:02.276
but I think the WikiProject[br]you're starting on Wikidata

0:23:02.276,0:23:03.671
will do that--

0:23:03.671,0:23:07.671
is all to engage people[br]not working about India directly,

0:23:07.671,0:23:12.607
but like I have tools, names,[br]but I don't deal with Indian names

0:23:13.139,0:23:17.139
because I am not sure I understand[br]all there are on them,

0:23:17.139,0:23:19.725
and I don't want to do[br]something massively wrong,

0:23:19.725,0:23:21.375
so better to be careful.

0:23:21.375,0:23:26.624
But I just need to ask with someone[br]who understand all the problems,

0:23:26.624,0:23:28.984
and I can add an automated tool

0:23:28.984,0:23:32.811
and deal with thousands[br]upon thousands of items.

0:23:33.047,0:23:35.727
And I think they are many, many tools

0:23:35.727,0:23:39.687
already doing some automated description[br]and things like that

0:23:39.687,0:23:46.104
for which we don't actually[br]need people every day,

0:23:46.104,0:23:50.918
we just need like 10 minutes time[br]for someone to tell me

0:23:50.918,0:23:54.778
or to say family names in those languages,

0:23:54.778,0:23:57.278
and then it just added to the tool.

0:23:57.278,0:24:01.917
And you probably know[br][automated] description tool,

0:24:02.520,0:24:06.750
but if you just ask the people[br]who are using it massively

0:24:06.750,0:24:08.810
to just add Indian languages,

0:24:08.810,0:24:12.810
then you have all Wikidatans[br]doing the same work for you,

0:24:12.810,0:24:15.409
and actually, it is a problem.

0:24:16.969,0:24:20.970
I am helping an African community[br]build up their Wikidata

0:24:20.970,0:24:23.819
in Wikipedia, so it's not[br]the same problem,

0:24:23.819,0:24:25.737
but nearly the same problem.

0:24:26.317,0:24:29.347
And that's the problem we have

0:24:29.347,0:24:32.257
which is actually bridging the gap

0:24:32.257,0:24:35.119
between the biggest Wikidatans--

0:24:35.813,0:24:39.313
I am doing works in languages[br]I don't know a word of,

0:24:40.843,0:24:44.526
but it's this kind of adoption system,

0:24:44.526,0:24:48.566
like I need a native speaker to tell me

0:24:48.566,0:24:51.854
what I can do with all the problems[br]on all the complicated cases.

0:24:51.854,0:24:55.752
And everything[br]that I can automate, I will automate.

0:24:55.752,0:24:59.752
And it's just an idea,[br]but do you think it will be like

0:24:59.752,0:25:04.752
a good idea to create not so specific[br]Wiki knowledge gap on Wikidata,

0:25:04.752,0:25:09.462
but a matching system

0:25:09.462,0:25:15.821
like, "Hey I am working on this subject,[br]do you want to ask me for that?"

0:25:15.821,0:25:20.521
- Like, yeah, a matching tool, like to--[br]- Connect people.

0:25:20.521,0:25:22.980
- (woman 3) To connect people[br]across languages.

0:25:24.119,0:25:26.910
Yeah. So that was my idea[br]because I think

0:25:27.253,0:25:30.273
some of the African communities[br]I am helping,

0:25:30.273,0:25:33.177
would really, really love[br]what you're doing,

0:25:33.177,0:25:39.253
but none of them speak Indian,[br]and we just need to have pivot people

0:25:39.746,0:25:41.266
to create the link

0:25:41.523,0:25:44.504
and make all this even more powerful.

0:25:44.504,0:25:47.341
And I really, really love[br]what you're doing. So thank you.

0:25:47.341,0:25:48.551
Thank you so much.

0:25:48.551,0:25:50.619
Thanks to Bodhi for all the awesome work.

0:25:51.341,0:25:52.728
(laughs)

0:25:52.728,0:25:54.498
And the larger Indian community.

0:25:54.498,0:25:57.113
But that's a really good idea,[br]I think we should take that up.

0:25:57.683,0:26:03.090
As a movement, we have not been doing[br]the sharing thing pretty good.

0:26:03.090,0:26:04.840
We need to figure out how to do that.

0:26:04.840,0:26:06.727
Because there are awesome tools,

0:26:06.727,0:26:09.028
one is built, but the others[br]don't know about.

0:26:09.028,0:26:10.864
That's a larger problem,

0:26:10.864,0:26:14.197
and that's a piece that fits[br]into the larger problem.

0:26:14.197,0:26:16.061
We should be solving someplace.

0:26:16.061,0:26:18.050
Let's figure out where we can do that.

0:26:19.421,0:26:20.867
Thank you.

0:26:20.867,0:26:23.027
(applause)