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Bonus Video: Ian Horrocks / RDF / OWL (Advanced)

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Using Python to Access Web Data

Universidad de Míchigan

4.8 (20,835 calificaciones) | 230K estudiantes inscritos

Curso 3 de 5 en Python para todos Programa Especializado

This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.

Destrezas que aprenderás

Json, Xml, Python Programming, Web Scraping

Revisiones

4.8 (20,835 calificaciones)

5 stars
17,281 ratings
4 stars
2,982 ratings
3 stars
458 ratings
2 stars
65 ratings
1 star
49 ratings

AG

Sep 13, 2017

The course teaches the basics of how to interact with the internet using Python. The course is simple, well explained, has great interviews with people who were part of the creation of the internet.

HA

Jun 14, 2019

Excellent course, Dr Chuck is amazing in delivering the content of this course in a very unique manner, a great deal of practice and examples and sample code to help students throughout the course

De la lección

Web Services and XML (Chapter 13)

In this section, we learn how to retrieve and parse XML (eXtensible Markup Language) data.

Interview: Roy Fielding - Understanding the REST Architecture10:53

Bonus: Office Hours - Boston2:01

Bonus Video: Ian Horrocks / RDF / OWL (Advanced)7:56

Impartido por:

Charles Russell Severance
Professor

Transcripción

[MUSIC]

My background had been working in medical informatics and developing

what we would now call ontology languages and reasoning systems.

Although actually to be honest in the medical informatics area,

we weren't necessarily calling them ontologies back then, we do now.

And I went to a meeting of a European Network,

met people like Franklin Hamlin, Dieter Fencil,

who were also interested in the beginnings of this area.

And I managed to convince them that this description logics area, which I'd

been working in, which is a sort of logic whose rationale is to formalize

what we now call ontology languages, that that would be a good starting point.

It had more expressive power and

a very clear formal semantics with just first-order logic, basically.

Just a fragment of first-order logic.

And we went from there.

And between us, we developed a language called OIL,

which was based on a description logic which was already around at the time.

We met people in the U.S. like Jim Hendler, and the DAML program,

people working on the DAML program. We all decided that, hey, we're more or

less trying to do the same thing, why don't we pool our resources, which we did.

Came up with DAML+OIL, wasn't really much different from the OIL thing.

And then the idea was to go for, to try to develop this into a standard

so more people would really be able to use it.

And this was where OWL originally came in, and

then the OWL working group started and we went through the process.

What we thought would be the easy process of standardizing DAML+OIL,

as a Web ontology language.

So then of course, a whole new bunch of people joined the party,

which were the sort of Web people.

And of course, they had a whole load of concerns of their own.

Things that were important to them.

Which were things like, integration compatibility with RDF

and generally with Web infrastructure and existing standards.

So actually the process then of changing DAML+OIL, evolving DAML+OIL into OWL,

it took longer than we thought, involved a bigger change than we thought,

and I think it took a couple of years in the end.

And much more than that off my life, [LAUGH] ten years off my life, I think.

And but, I mean,

it was pretty interesting, and I learned a lot there, as well.

And the language evolved not a great deal,

but the few, it was mainly the syntax and the relationship with RDF that changed.

The underlying logic didn't change very much.

And of course the semantics didn't change.

Because that just flows from the logic.

The huge impact of OWL was just the fact that being these kind of

KR languages around for donkey's years, as you know.

And but there'd been, you know, every university

research group had typically created their own variant,

their own little flavor, all somewhat incompatible.

And it had been conjectured that that had interfered with the take up.

But of course, you could never really tell whether or not there would be significant

take up if you had a standard language that everybody was using

until OWL came along, when suddenly we had that thing.

We had a standard KR language, that was

kind of supported by lots of different groups building infrastructure.

And suddenly applications people started to feel more comfortable about using that.

It had always been an issue you know,

if you were using the system from the University of X, and then that research

group just suddenly got bored with that and went off and did something else.

So you were left with no support, whatever.

Now with OWL you could use a standard language,

there were tons of people supporting it.

Ever growing array of infrastructure to support

building, deploying, maintaining ontologies,

and that meant that people then really started using it,

I mean, people in industry to some extent, but

initially probably more other academic disciplines, scientists, researchers.

>> Did the whole industry sort of form, that you wouldn't have anticipated?

>> Yeah, >> Once you got the plumbing kind of right?

>> But I mean, I think the thing struck me initially about OWL was the fact that,

once we just agreed we were all gonna use 15-mil pipes,

then we could have a huge industry of people building all kinds of cool

plumbing stuff that could all fit together and

do amazing stuff, that we would never have anticipated in advance.

And was the thing about OWL, it's not that OWL was the perfect language,

it's not that 15 mil is the right size for a pipe.

It's just that some point, you have to say, okay, you know,

I would have gone for 18 mil, you would have gone for

13 mil, we'll compromise here, we'll fix on 15 mil, we'll get on with it.

And we'll see what we can build with that.

And this is what happened with OWL.

There was huge arguments, in the end we managed to reach

a compromise that everybody could sign up to.

Some with more grumbling than others, but everybody signed up to it.

And then people started building stuff.

And now the thing that amazes me today, with both OWL and RDF actually, is that

you just bump into people all the time who are just building applications, using

OWL and RDF tools and infrastructure, that you never even knew about before.

Because that stuff just works now.

They just download it off the Web, they build it into applications,

they're pretty happy.

It may not be.

If you look at what they did, you may say well,

you know, if I were doing that I'm not even sure I would have really used RDF and

OWL, but they downloaded the tools, worked really nicely.

They were super happy with it and the toilet flushed in the end.

Job done.

>> Job done.

>> And I think one of the things as a community we haven't been very good about

is embracing that as a great success.

And saying, yeah we've done a really good job here.

We've built stuff that people are using and it works.

It really kind of works off the shelf these days.

The tools and stuff are pretty robust.

And we should be proud of that, it's a achievement of the community.

[MUSIC]

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