Fantastic.
Welcome.
So I'm going to cover a lot and I have a set of friends in India and around the world
watching. So it's being live screen. So welcome in India. It's 10,000 kilometers away.
So they can't be heard over here.
But I will be showing some clips and then. Okay, off we go.
So I'm talking about knowledge graphs and semantic time.
How many people have used knowledge graphs?
Good. I want to pick your brains.
Right. So this is an open, a free and open source session.
So we have to have mascots.
So these are these are the mascots that you know and love.
But the open knowledge foundation,
around by Sarah, has got a mascot,
Chuff the Ocarpy and we've talking more about that.
And we have our own mascot, Amy the kangaroo.
She couldn't come because of Brexit.
Stuff and also.
Right.
So it's going to be fun, but we are deadly serious.
Hundreds of millions of people are going to die because of climate.
And some of those will die because they don't have the knowledge they need.
And we are appalling the bad at getting knowledge out to people in the 21st century.
So semantic knowledge is one of the ways to try and get believable,
verifiable knowledge out to the world.
So very serious.
I don't know anybody else who's getting this out.
Guterious has called this manual.
I'm going to be showing you the manual for human survival.
And nobody's interested in it.
Right. So we're a community.
We collect climate information.
We make it semantic.
So machines can understand it.
We annotate it.
And we do it for all sorts of purposes.
Learning, reuse, advocacy.
But we actually want people to use this.
So please, if you're anybody who's interested in climate.
And you all should be.
Have a look at this material because it's critical to all of us.
Here's us in India earlier this year.
Covered with flowers.
And here's Frater Hector.
You'll be hearing a little clip from here.
And Frater will be talking at ORDM in two weeks time.
Here are the people who make this possible.
National Institute for Plantry, Nome Research in Delhi.
Frater's at Ashoka University.
And Amberine, one of our earlier volunteers, is in Kashmir.
And Frater has been developing software.
She's now handing over to Amberine in what I call a doctor who model of open source.
And that's one thing you should look at for succession of projects.
We have five years and we have about 100 people
who've been through our system.
Just been joined by six new interns.
There are only just three weeks in.
And each of them picks a chapter of this massive IPCC material
into governmental panel on climate change.
And you can see that they've all picked different chapters here.
So it's a very nice, parallelizable effort.
Each of them does a chapter, each applies the same technology
to the chapter and then contributes back.
And it's a wonderful learning process and a wonderful way of debugging our software,
which needs a lot of debugging.
And Sarah and at the bottom will be presenting.
We do what we call learning by doing.
Everybody here learns by doing right.
So there are no lectures.
But we do meet a lot online.
We meet in the early stages of the interns.
Two hours a day, every day, every week, they say five days a week,
where we review the project.
We will review the software and so on.
But then as the project goes through its scales,
and people do things offline.
And you're going to see a very nice contribution from Sharon.
And she's done a lot of this online.
Right.
So let's get straight in.
I'm going to show you two presentations at the tech we want,
which is what Sarah organized and ran very well.
About two months ago from the Open Knowledge Foundation.
So I'm going to show you a bit of data and then a bit of a paradigm.
This is in a situation if you haven't heard the word.
It's not.
Yes, you need to put it on the screen.
No.
It should have been mirrored, but it's not mirrored.
Oh, maybe you are in presentation.
Yes.
I'm in presentation.
So if you escape here, I guess.
Yeah, that's what?
That's working.
Okay.
No problem.
I've had much worse in the server.
Yes.
Exactly.
Right.
So let's see if we can get straight back.
He's in the top right.
She should be bigger, but.
Five minutes.
Talk about what's in the kitchen that's been there.
And I let you all decide.
It's sustainable.
All right.
So.
There's so much knowledge.
Kind of knowledge out there.
I don't know if it's also mentioning.
I guess you see reports, for example,
could be thousands of pictures stood in juckin.
And your next liberty duckin is at either cop blockin.
And some also thousands of pictures long.
Um.
They don't know.
Here's just talking about Tim Berners Lee's five star documents.
Have any of you come across five star documents?
An engine.
And you should.
Because it's critical.
For at least it's in the course.
You want to visit the course.
And tell it to visit your engine.
In this direction, which is different.
At PCC website.
You want to see the website.
Um.
And at PCC.
Lottery.
All.
One.
And articles.
In we live five gig papers.
They.
Um.
Turn these supports into something that's semantic.
And annotations.
Uh.
With the help of dictionaries.
And make them five star documents.
So.
I start dictionary.
For example, as you can see.
This is an example that we've made.
Has got.
And then unique identity funders.
But that's to it.
Making it five star.
Um.
In keeping.
And it's.
But unique.
And it comes to like.
From.
And.
How many of you are involved with.
Wikidata.
Good.
I need to.
Wonderful.
I need to talk to afterwards.
I really need.
Wikidata friends.
Right.
Okay.
Um.
Back to the presentation.
Um.
Right.
We'll feel a little bit of paragat.
It's got to go back to presentation.
That's.
So.
Okay.
Well.
Don't worry.
So.
It should be this one.
No.
Yeah.
This one.
Good.
So.
So this is paragat.
Now.
One.
I don't.
Actually had no coding at all.
Um.
And she learned all this.
It's okay.
Okay.
It's.
In my demo.
Yes.
Okay.
Then.
I.
And this.
And.
Well.
So.
We have.
Whatever.
So.
Me.
I'll have a.
So.
of the chapters and we have the clean content that is the HTML semantic version of the
documents. Similarly, the UNF 60 that is United Nations Framework Convention of the Climate
Change have cost that we need to manually dump as a metric climate. We have data taken
for the UNF 60 report which indexes all, you know, which can browse everything. And all
the reports have been converted into semi-flipped documents.
Okay, well, we don't think it is. These are 15 minutes long, so we'll skip those. But they're
online and you should look at them. Oh, presentation, that's fine. Okay, on we go. So, whatever
his faults, Richard Solomon came up with the four freedoms and these are critical. And
they're not only critical to software, but they've also, I think, spread across the world.
So, they help to launch the human genome as an open project and many projects look
back to software freedoms as probably one of the, how many? No problem. No problems. And so,
this has infected positively all our thinking in the future. So, our mission is to create
very fireball, computable climate knowledge. By very fireball, I mean, you can say, what authority
produced it and to be sure that it's a material that comes out of that authority? It doesn't
mean it's right. I mean, they say it's going to flood in 10 years time, you know, that may
or may not happen, but that is a true statement from them. This is all that's on it, a huge amount
of stuff, 20,000 pages, hundreds of thousands of links and so on. And so we're going to put this
in a knowledge graph. Knowledge graphs can be quite complex. We use a simple idea which is
idea of cripples, subject, predicate, object, node, edge, node. So, we're going to visualize
this, we're going to see network x from paradigms. And not from paradigms, not from fan,
and join us just two or three weeks ago.
Why are you shy? And when you tell an asymptotic length, I would like to tell you how I need
networks of person working through paradigms. With existing eye-to-sense website, we get from them
where they're different working with a paradigms. We look one, two, three. And each working
of consists of reports, resources, means, and various paradigms. If we look at a board of working
with one, our looks look looks like we get the more that they're different things presently.
They can get somebody somewhere for medicine, make up, fact sheets, and the most important is
chapezin. They have different chapters which are presently each working with them. Working with
the board. I used to convert this working work in the network. A project is divided into
the board. The first part is leading this edge team of the body. And what we need to work
simpler for my dead edge team. By the help of the Jason command, find, and then we need
all the unnecessary desks, tags that are not required for networking. And we need to
ask them to find which looks very simple. They can answer this. Our second part of the project
is to take this desks of desks to find and convert it into a network. To deal with the
part, this is our edge team and find. Now, network is moving with the help of network x.
Network x is the five advantage. It has manipulation, addition, definition of node, addition,
edges, and vision of vision, networks. I have added nodes to our network. I did edge to our network
taking an account of all the list, listed with this item, which has text in it. And then,
we have visualized it with the help of network x. Let's figure out what is in this video.
Presenting network x. After people to see how network groups, I have saved enough
the energy for my fight. If I show you how my network groups will be something like this,
this is the network of working group, but that's it. You have chapters where the code
will be asked, and this is an recognition. If you stop there, the video goes on. But this network
is computed by network x. Has anyone used network x? Excellent. I'll be talking to you.
And it's not very obvious there are little arrows on this, so it's a directed graph.
This is the material within the chapter. We plan to do this for the whole of the material.
Now, what I'm going to do now is I'm going to go on to, I'll just mention linked open data.
There are 100 million plus nodes in open linked data and wicked data around the world.
A huge amount of material, and it's not, I think, five minutes. We are consuming this,
and we're also hoping to put stuff back into it. But we haven't been able to get the IPCC to
agree copyright of their material. They are not an easy material organization to deal with.
So, I'm going to show you first three things very quickly, and then I'll be booted off.
So, this is a 500 term, okay. This is a glossary which Simon Worthington has put together
from their material, and well, have a little look at it. Come on.
Or maybe it's not going to work. Oh, yes. So, he's turned it from some horrible web pages
into something which is very practical. It's all times that we've added wicked media to it,
and you can see it's also Renew has put in equivalence in Hindi there. So, the idea is that you
have a mini-in-site, a piggy of climate, which grows by people adding things into it.
That's Simon. I'm getting close to the end. Clearly, I think our finish with
pages, knowledge graph, because it's pretty impressive. So, straighter, and as I said,
she'd had no previous. You need to have the human in the loop and the human. Exactly.
And animals give love, but they're not very good on the keyboard. What's happening here,
it's got something like 23,000 nodes from the glossary. It's loading them in, and I think it's
being displayed with obsidian, but any network display would do that, and it's working out the
light layout. And here we go. That's the knowledge graph, and you zoom in, and you can see
that you've got his climate in the middle, and you can see climate model, global warming, all these things
are linked to it. The links don't sharp well on the screen, but believe me, that there's our.
And so, you have a huge and computable object. I don't have time at tall to talk about what
Aegon Velligarkin has done. Aegon is from Masterick, and he and I met yesterday in Tilburg,
and he's done a brilliant job of taking the IPCC material and turning it into many knowledge graphs.
So, it's all constantly better than I had expected. It's one of anything else, and that's
more or less. You need to get back into the. Yes. So, we would like to interact with you. We want
your questions. We want your technology. We want you to use our technology. We want to use your
technology, and so on, because there is not enough being done for climate in this way. We are not
creating a 21st century version of climate knowledge. We're still looking about 50 years out of
date. And finally, I will say that we would like tough to make friends again with O.K.F.
There ya'll Soror. I have made a special phone holder with O.K.F.F.F.F.F. and that is yours.
If you promise to give
off some more exposure, right.
So, thank you. Thank you. So, we have a few minutes for
For question now, it is very important Peter that when you take question, you repeat the question or at this part of it for the people on land.
Yeah.
In the mic.
Does it have questions?
Yes.
What are the most impact from projects or initiatives that use your knowledge ground today?
What are the most important projects?
Is it used your knowledge ground today?
There aren't any.
That's why we're asking you to come and work with us.
We are creating an infrastructure and we want people to use it.
And I just find it's deafening silence over climate.
Nobody is actually interested and the water is creeping up in Cambridge.
It will flood the villages.
If there is one thing that's something used to this, we start to do.
What would you recommend they do?
It depends on their skills.
I'm sorry.
If you're new to this, what are the opportunities?
They're huge.
So you can document things that's very valuable, make videos, present things, or if you're a coder, you can run our unit.
We have a thousand unit tests on this.
There's a lot of code here.
If you're a coder, test it and see what you can do.
And then get ideas for how you can integrate it with other things.
We probably need some gooeys.
I'm looking into street niche and things of that sort.
But it spreads out very nicely.
Thank you for your talk.
I've got a question about the actuality.
You've indexing data is important, but it assumes that the data is available.
And we see in the United States, for instance, diversity and equity data is clicked out of websites.
And maybe we don't know, but with the as crazy as maybe.
Biamet based data could be also clicked from websites.
So do you have any link with our capital projects, such as our capital or things like this,
to preserve the job beyond just assuming it's available?
Okay. The question is that data is disappearing because of policies in the United States.
How do we preserve this? Well, we are taking this from the United Nations.
The United States might describe the United Nations in which, of course, I think the world has a serious problem.
But until that happens, the United States runs its websites independent of the US government.
So that will preserve, as long as anything preserves.
We put everything we can on GitHub, on the node.
You know, everything we do is open and put us onto the web as soon as we do it.
Oh, we call it open notebook science.
Well, so can we get stories told by knowledge graphs, right?
I think we need to have a human in that.
The knowledge graph will answer sparkle queries.
Sparkles are not very human-friendly.
My colleague Agon can write them just like that, but I can't.
And we need a better interface to sparkle queries.
But sparkle and graph QL are the tools which will query knowledge graphs and so on.
And the knowledge graphs are easy to link to other resources.
Either the link to open data cloud of 100 million nodes or to websites elsewhere, which are open.
And then I think the modern AI so-called tools will be able to help summarize and so on.
But I've had bad hallucinations with AI.
And I would not cross the future of the world to AI at the moment.
Okay, I'm sorry.
We have a really tight schedule and science coming from the door.
Tell me to stop the questions.
We need to welcome our next speaker.
I'd like it.