WEBVTT

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Fantastic.

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Welcome.

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So I'm going to cover a lot and I have a set of friends in India and around the world

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watching. So it's being live screen. So welcome in India. It's 10,000 kilometers away.

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So they can't be heard over here.

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But I will be showing some clips and then. Okay, off we go.

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So I'm talking about knowledge graphs and semantic time.

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How many people have used knowledge graphs?

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Good. I want to pick your brains.

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Right. So this is an open, a free and open source session.

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So we have to have mascots.

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So these are these are the mascots that you know and love.

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But the open knowledge foundation,

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around by Sarah, has got a mascot,

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Chuff the Ocarpy and we've talking more about that.

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And we have our own mascot, Amy the kangaroo.

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She couldn't come because of Brexit.

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Stuff and also.

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Right.

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So it's going to be fun, but we are deadly serious.

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Hundreds of millions of people are going to die because of climate.

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And some of those will die because they don't have the knowledge they need.

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And we are appalling the bad at getting knowledge out to people in the 21st century.

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So semantic knowledge is one of the ways to try and get believable,

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verifiable knowledge out to the world.

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So very serious.

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I don't know anybody else who's getting this out.

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Guterious has called this manual.

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I'm going to be showing you the manual for human survival.

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And nobody's interested in it.

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Right. So we're a community.

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We collect climate information.

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We make it semantic.

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So machines can understand it.

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We annotate it.

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And we do it for all sorts of purposes.

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Learning, reuse, advocacy.

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But we actually want people to use this.

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So please, if you're anybody who's interested in climate.

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And you all should be.

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Have a look at this material because it's critical to all of us.

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Here's us in India earlier this year.

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Covered with flowers.

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And here's Frater Hector.

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You'll be hearing a little clip from here.

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And Frater will be talking at ORDM in two weeks time.

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Here are the people who make this possible.

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National Institute for Plantry, Nome Research in Delhi.

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Frater's at Ashoka University.

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And Amberine, one of our earlier volunteers, is in Kashmir.

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And Frater has been developing software.

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She's now handing over to Amberine in what I call a doctor who model of open source.

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And that's one thing you should look at for succession of projects.

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We have five years and we have about 100 people

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who've been through our system.

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Just been joined by six new interns.

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There are only just three weeks in.

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And each of them picks a chapter of this massive IPCC material

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into governmental panel on climate change.

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And you can see that they've all picked different chapters here.

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So it's a very nice, parallelizable effort.

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Each of them does a chapter, each applies the same technology

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to the chapter and then contributes back.

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And it's a wonderful learning process and a wonderful way of debugging our software,

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which needs a lot of debugging.

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And Sarah and at the bottom will be presenting.

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We do what we call learning by doing.

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Everybody here learns by doing right.

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So there are no lectures.

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But we do meet a lot online.

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We meet in the early stages of the interns.

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Two hours a day, every day, every week, they say five days a week,

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where we review the project.

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We will review the software and so on.

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But then as the project goes through its scales,

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and people do things offline.

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And you're going to see a very nice contribution from Sharon.

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And she's done a lot of this online.

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Right.

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So let's get straight in.

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I'm going to show you two presentations at the tech we want,

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which is what Sarah organized and ran very well.

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About two months ago from the Open Knowledge Foundation.

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So I'm going to show you a bit of data and then a bit of a paradigm.

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This is in a situation if you haven't heard the word.

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It's not.

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Yes, you need to put it on the screen.

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No.

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No.

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It should have been mirrored, but it's not mirrored.

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Oh, maybe you are in presentation.

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Yes.

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I'm in presentation.

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So if you escape here, I guess.

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Yeah, that's what?

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That's working.

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Okay.

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No problem.

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I've had much worse in the server.

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Yes.

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Exactly.

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Right.

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So let's see if we can get straight back.

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He's in the top right.

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She should be bigger, but.

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Five minutes.

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Talk about what's in the kitchen that's been there.

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And I let you all decide.

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It's sustainable.

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All right.

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So.

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There's so much knowledge.

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Kind of knowledge out there.

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I don't know if it's also mentioning.

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I guess you see reports, for example,

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could be thousands of pictures stood in juckin.

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And your next liberty duckin is at either cop blockin.

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And some also thousands of pictures long.

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Um.

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They don't know.

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Here's just talking about Tim Berners Lee's five star documents.

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Have any of you come across five star documents?

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An engine.

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And you should.

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Because it's critical.

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For at least it's in the course.

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You want to visit the course.

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And tell it to visit your engine.

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In this direction, which is different.

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At PCC website.

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You want to see the website.

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Um.

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And at PCC.

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Lottery.

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All.

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One.

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And articles.

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In we live five gig papers.

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They.

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Um.

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Turn these supports into something that's semantic.

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And annotations.

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Uh.

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With the help of dictionaries.

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And make them five star documents.

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So.

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I start dictionary.

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For example, as you can see.

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This is an example that we've made.

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Has got.

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And then unique identity funders.

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But that's to it.

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Making it five star.

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Um.

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In keeping.

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And it's.

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But unique.

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And it comes to like.

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From.

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And.

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And.

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And.

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How many of you are involved with.

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Wikidata.

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Good.

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I need to.

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Wonderful.

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I need to talk to afterwards.

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I really need.

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Wikidata friends.

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Right.

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Okay.

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Um.

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Back to the presentation.

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Um.

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Right.

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We'll feel a little bit of paragat.

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It's got to go back to presentation.

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That's.

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So.

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Okay.

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Well.

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Don't worry.

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So.

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It should be this one.

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No.

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No.

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Yeah.

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This one.

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Good.

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So.

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So this is paragat.

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Now.

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One.

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I don't.

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Actually had no coding at all.

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Um.

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And she learned all this.

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It's okay.

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Okay.

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Okay.

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It's.

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It's.

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It's.

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It's.

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It's.

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In my demo.

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Yes.

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Okay.

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Then.

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I.

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And this.

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And.

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And.

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Well.

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So.

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We have.

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Whatever.

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So.

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Me.

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I'll have a.

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So.

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So.

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of the chapters and we have the clean content that is the HTML semantic version of the

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documents. Similarly, the UNF 60 that is United Nations Framework Convention of the Climate

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Change have cost that we need to manually dump as a metric climate. We have data taken

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for the UNF 60 report which indexes all, you know, which can browse everything. And all

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the reports have been converted into semi-flipped documents.

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Okay, well, we don't think it is. These are 15 minutes long, so we'll skip those. But they're

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online and you should look at them. Oh, presentation, that's fine. Okay, on we go. So, whatever

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his faults, Richard Solomon came up with the four freedoms and these are critical. And

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they're not only critical to software, but they've also, I think, spread across the world.

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So, they help to launch the human genome as an open project and many projects look

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back to software freedoms as probably one of the, how many? No problem. No problems. And so,

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this has infected positively all our thinking in the future. So, our mission is to create

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very fireball, computable climate knowledge. By very fireball, I mean, you can say, what authority

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produced it and to be sure that it's a material that comes out of that authority? It doesn't

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mean it's right. I mean, they say it's going to flood in 10 years time, you know, that may

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or may not happen, but that is a true statement from them. This is all that's on it, a huge amount

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of stuff, 20,000 pages, hundreds of thousands of links and so on. And so we're going to put this

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in a knowledge graph. Knowledge graphs can be quite complex. We use a simple idea which is

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idea of cripples, subject, predicate, object, node, edge, node. So, we're going to visualize

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this, we're going to see network x from paradigms. And not from paradigms, not from fan,

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and join us just two or three weeks ago.

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Why are you shy? And when you tell an asymptotic length, I would like to tell you how I need

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networks of person working through paradigms. With existing eye-to-sense website, we get from them

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where they're different working with a paradigms. We look one, two, three. And each working

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of consists of reports, resources, means, and various paradigms. If we look at a board of working

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with one, our looks look looks like we get the more that they're different things presently.

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They can get somebody somewhere for medicine, make up, fact sheets, and the most important is

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chapezin. They have different chapters which are presently each working with them. Working with

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the board. I used to convert this working work in the network. A project is divided into

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the board. The first part is leading this edge team of the body. And what we need to work

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simpler for my dead edge team. By the help of the Jason command, find, and then we need

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all the unnecessary desks, tags that are not required for networking. And we need to

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ask them to find which looks very simple. They can answer this. Our second part of the project

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is to take this desks of desks to find and convert it into a network. To deal with the

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part, this is our edge team and find. Now, network is moving with the help of network x.

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Network x is the five advantage. It has manipulation, addition, definition of node, addition,

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edges, and vision of vision, networks. I have added nodes to our network. I did edge to our network

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taking an account of all the list, listed with this item, which has text in it. And then,

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we have visualized it with the help of network x. Let's figure out what is in this video.

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Presenting network x. After people to see how network groups, I have saved enough

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the energy for my fight. If I show you how my network groups will be something like this,

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this is the network of working group, but that's it. You have chapters where the code

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will be asked, and this is an recognition. If you stop there, the video goes on. But this network

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is computed by network x. Has anyone used network x? Excellent. I'll be talking to you.

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And it's not very obvious there are little arrows on this, so it's a directed graph.

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This is the material within the chapter. We plan to do this for the whole of the material.

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Now, what I'm going to do now is I'm going to go on to, I'll just mention linked open data.

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There are 100 million plus nodes in open linked data and wicked data around the world.

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A huge amount of material, and it's not, I think, five minutes. We are consuming this,

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and we're also hoping to put stuff back into it. But we haven't been able to get the IPCC to

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agree copyright of their material. They are not an easy material organization to deal with.

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So, I'm going to show you first three things very quickly, and then I'll be booted off.

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So, this is a 500 term, okay. This is a glossary which Simon Worthington has put together

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from their material, and well, have a little look at it. Come on.

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Or maybe it's not going to work. Oh, yes. So, he's turned it from some horrible web pages

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into something which is very practical. It's all times that we've added wicked media to it,

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and you can see it's also Renew has put in equivalence in Hindi there. So, the idea is that you

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have a mini-in-site, a piggy of climate, which grows by people adding things into it.

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That's Simon. I'm getting close to the end. Clearly, I think our finish with

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pages, knowledge graph, because it's pretty impressive. So, straighter, and as I said,

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she'd had no previous. You need to have the human in the loop and the human. Exactly.

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And animals give love, but they're not very good on the keyboard. What's happening here,

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it's got something like 23,000 nodes from the glossary. It's loading them in, and I think it's

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being displayed with obsidian, but any network display would do that, and it's working out the

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light layout. And here we go. That's the knowledge graph, and you zoom in, and you can see

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that you've got his climate in the middle, and you can see climate model, global warming, all these things

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are linked to it. The links don't sharp well on the screen, but believe me, that there's our.

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And so, you have a huge and computable object. I don't have time at tall to talk about what

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Aegon Velligarkin has done. Aegon is from Masterick, and he and I met yesterday in Tilburg,

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and he's done a brilliant job of taking the IPCC material and turning it into many knowledge graphs.

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So, it's all constantly better than I had expected. It's one of anything else, and that's

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more or less. You need to get back into the. Yes. So, we would like to interact with you. We want

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your questions. We want your technology. We want you to use our technology. We want to use your

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technology, and so on, because there is not enough being done for climate in this way. We are not

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creating a 21st century version of climate knowledge. We're still looking about 50 years out of

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date. And finally, I will say that we would like tough to make friends again with O.K.F.

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There ya'll Soror. I have made a special phone holder with O.K.F.F.F.F.F. and that is yours.

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If you promise to give

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off some more exposure, right.

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So, thank you. Thank you. So, we have a few minutes for

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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.

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Yeah.

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In the mic.

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Does it have questions?

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Yes.

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What are the most impact from projects or initiatives that use your knowledge ground today?

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What are the most important projects?

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Is it used your knowledge ground today?

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There aren't any.

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That's why we're asking you to come and work with us.

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We are creating an infrastructure and we want people to use it.

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And I just find it's deafening silence over climate.

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Nobody is actually interested and the water is creeping up in Cambridge.

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It will flood the villages.

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If there is one thing that's something used to this, we start to do.

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What would you recommend they do?

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It depends on their skills.

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I'm sorry.

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If you're new to this, what are the opportunities?

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They're huge.

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So you can document things that's very valuable, make videos, present things, or if you're a coder, you can run our unit.

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We have a thousand unit tests on this.

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There's a lot of code here.

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If you're a coder, test it and see what you can do.

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And then get ideas for how you can integrate it with other things.

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We probably need some gooeys.

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I'm looking into street niche and things of that sort.

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But it spreads out very nicely.

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Thank you for your talk.

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I've got a question about the actuality.

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You've indexing data is important, but it assumes that the data is available.

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And we see in the United States, for instance, diversity and equity data is clicked out of websites.

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And maybe we don't know, but with the as crazy as maybe.

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Biamet based data could be also clicked from websites.

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So do you have any link with our capital projects, such as our capital or things like this,

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to preserve the job beyond just assuming it's available?

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Okay. The question is that data is disappearing because of policies in the United States.

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How do we preserve this? Well, we are taking this from the United Nations.

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The United States might describe the United Nations in which, of course, I think the world has a serious problem.

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But until that happens, the United States runs its websites independent of the US government.

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So that will preserve, as long as anything preserves.

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We put everything we can on GitHub, on the node.

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You know, everything we do is open and put us onto the web as soon as we do it.

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Oh, we call it open notebook science.

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Well, so can we get stories told by knowledge graphs, right?

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I think we need to have a human in that.

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The knowledge graph will answer sparkle queries.

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Sparkles are not very human-friendly.

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My colleague Agon can write them just like that, but I can't.

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And we need a better interface to sparkle queries.

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But sparkle and graph QL are the tools which will query knowledge graphs and so on.

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And the knowledge graphs are easy to link to other resources.

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Either the link to open data cloud of 100 million nodes or to websites elsewhere, which are open.

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And then I think the modern AI so-called tools will be able to help summarize and so on.

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But I've had bad hallucinations with AI.

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And I would not cross the future of the world to AI at the moment.

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Okay, I'm sorry.

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We have a really tight schedule and science coming from the door.

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Tell me to stop the questions.

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We need to welcome our next speaker.

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I'd like it.

