Semantic Web, an introduction

Applied Semantic WebTimely. Practical. Reliable.http://applied-semantic-web.org

Semantic WebAn IntroductionEmanuele Della [email protected]://emanueledellavalle.org

Emanuele Della Valle - http://applied-semantic-web.org

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2


Agenda

Un modello per studiare l’innovazione

Dal Web delle origini al Semantic Web

Introduzione al Semantic Web

Applicazioni del Semantic Web

Conclusione

3

Emanuele Della Valle - http://applied-semantic-web.org 4

Innovazione


Innovazione oggi

idea

microfenomeno

macrofenomeno

problemi

innovarean

aliz

zare

creare

complessità = 6.000.000.000 persone


Innovazione: non tutto è controllabile

idea

microfenomeno

macrofenomeno

problemi

innovarean

aliz

zare

creare

complessità = magia


Innovazione: ruolo di scienza e ingegneria

idea

microfenomeno

macrofenomeno

problemi

innovarean

aliz

zare

creare

complessità = magia

scie

nza

ingegneria


Innovare …

idea

microfenomeno

innovare

creare

complessità


… non è mai solo una questione di tecnologia

idea

microfenomeno

innovare

soluzione tecnica

soluzione sociale

creare

complessità


Un modello per studiare l’innovazione oggi

idea

microfenomeno

macrofenomeno

problemi

anal

izza

re

creare

complessità

soluzione tecnica

soluzione sociale

innovare


Il Web delle origini 1988 (CERN)


Analizziamo il Web delle origini (1988-1998)

idea

microfenomeno

macrofenomeno

problemi

anal

izza

re

creare

complessità

soluzione tecnica

soluzione sociale

innovare

Non riesco ad accedere all’informazione Ipertesti + Internet

WWW

URI HTTP HTML

Condividere info e link a cose interessanti


Il Web delle origini (1990)

[source: http://ww

w.w

3.org/DesignIssues/diagram

s/history/proposal-fig1.gif ]

[source: http://ww

w.w

3.org/DesignIssues/diagram

s/history/Architecture_crop.png ]



idea

microfenomeno

macrofenomeno

problemi

anal

izza

re

creare

complessità

soluzione tecnica

soluzione sociale

innovare


WWW

URI HTTP HTML

Esplosione del fenomeno Web



The Web today

Let’s browse together 2009 Maphttp://www.zoomorama.com/01-2477f0e8b447bb6570493cdac464c41f



idea

microfenomeno

macrofenomeno

problemi

anal

izza

re

creare

complessità

soluzione tecnica

soluzione sociale

innovare


WWW

URI HTTP HTML

Esplosione del fenomeno Web

Come trovole pagine?

Come posso scrivere?



Come posso scrivere? … Web 2.0! (1998-2006)

idea

microfenomeno

macrofenomeno

problemi

anal

izza

re

creare

complessità

soluzione tecnica

soluzione sociale

innovare

Come posso scrivere?

wiki-wiki e Web-Log

Web 2.0

wiki blog

I fenomeni Wikipedia, blogosphere, …

Come gestiretutta questa

info? Condividere info e link a cose interessanti


Come trovo le pagine? … Google! (1998-oggi)

idea

microfenomeno

macrofenomeno

problemi

anal

izza

re

creare

complessità

soluzione tecnica

soluzione sociale

innovare

Come trovole pagine?

Indici + eigenspace

Google

PageRank

Il fenomeno Google

Basta cercare,vogliamo trovare!



Introduction

Computer should understand more

Large number of integrations - ad hoc - pair-wise

Too much information to browse, need for searching and mashing up automatically

Each site is “understandable” for us

Computers don’t “understand” much

?

Search & Mash-up Engine

010 0 1 1 0

01101

10100 10 0010 01 101 101 01 110 1 10 110 0 1 1 01 0 1 0 0 1 1 0 1 1 1 10 01 101 0 1

Millions of Applications


Introduction

What does “understand” mean?

What we say to Web agents

" For more information visit <a href=“http://www.ex.org”> my company </a> Web site. . .”

What they “hear”

" blah blah blah blah blah <a href=“http://www.ex.org”> blah blah blah </a> blah blah. . .”

Jet this is enought to train them to achive tasks for us

[ source http://www.thefarside.com/ ]


Introduction

What does Google “understand”?

Understanding that• [page1] links [page2] page2 is interesting

Google is able to rank results!• “The heart of our software is PageRank™, a system for

ranking web pages […] (that) relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page's value.”

http://www.google.com/technology/

21


Introduction

Two ways for computer to “understand”

Smarter machines• Such as

– Natural Langue processing (NLP)– Audio Processing– Image Processing (IP)– Video Processing– … many many more

• They all work fine alone, the problem is combining them– E.g., NLP meets IP

- NLP: What does your eye see?- IP: I see a sea- NLP: You see a “c”?- IP: Yes, what else could it be?

• Not the Semantic Web approach

Smarter Data• Make data easier for machines to publish, share, find and

understand– E.g. http://wordnet.rkbexplorer.com/description/word-sea vs.

http://wordnet.rkbexplorer.com/description/word-c • The Semantic Web approach

Some NLP Related Entertainment http://www.cl.cam.ac.uk/Research/ NL/amusement.html

22


Introduction The Semantic Web 1/4

“The Semantic Web is not a separate Web, but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.”

“The Semantic Web”, Scientific American Magazine, Maggio 2001 http://www.sciam.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21

Key concepts• an extension of the current Web• in which information is given well-defined meaning • better enabling computers and people to work in

cooperation.– Both for computers and people

23



“The Semantic Web is not a separate Web, but an extension of the current one […] ”

Web 1.0 The Web Today

24



“The Semantic Web […] , in which information is given well-defined meaning […]”

Human understandable but “only” machine-

readable

Human and machine

“understandable”

?

Web 1.0 Semantic Web

25



Semantic Web

Fewer Integration - standard - multi-lateral

[…] better enabling computers and

people to work in cooperation.

Even More Applications

Easier to understand for people

More “understandable” for computers

Semantic Mash-ups &Search


Introduction Semantic Web “layer cake”

Standardized

UnderInvestigation

Already Possible

[ source http://www.w3.org/2007/03/layerCake.png ]


Introduction The “lower” layers are already useful

Standardized

UnderInvestigation

Already Possible



Introduction Architectural view of the lower layers

[source http://www.w3.org/DesignIssues/diagrams/sw-double-bus.png ]


Introduction What’s an ontology?

Standardized

UnderInvestigation

Already Possible



Introduction

Ontology definition

Formal, explicit specification of a shared conceptualization

Machinereadable

Several peopleagrees that suchconceptual model

is adequate to describe such aspects of the

reality

A conceptual model of someaspects of the

realityIt makesdomain

assumptionexplicit

31

[Studer(98)]


Introduction

Esempio di Ontologia

Concetti e relazioni primitivi• essere umano• maschio• femmina• ha figlio

Concetti e relazioni derivate• un uomo è un essere umano ed

è un maschio• una donna è un essere umano ed

è una femmina• una madre è una donna che

ha almeno un figlio• una padre è un uomo che

ha almeno un figlio• un genitore è o un padre o una madre• un nonno è un uomo che

ha almeno un figlio che è un genitore • “essere figlio di” è la relazione inversa

a “avere un figlio”

Fatti asseriti• Antonio, Lorenzo e Carlo

sono uomini• Rosanna è una donna• Antonio ha figlio Lorenzo• Rosanna ha figlio Carlo

Carlo è figlio di LorenzoAntonio

Rosanna Lorenzo

Carlo

ha figlio

ha figlio

è figlio di

Una macchina in grado di “capire” un linguaggio ontologico “sa inferire”• Concetti: un nonno è un genitore• Fatti: Antonio è un nonno, Lorenzo è un padre, Rosanna è una madre


Introduction

How explicit shall the specification be?

“A little semantics, goes a long way”

[James Hendler, 2001]

“A little semantics, goes a long way”

[James Hendler, 2001]“A Little Semantic Web Goes a Long Way in Biology”[Wolstencroft et al., 2005]

“A Little Semantic Web Goes a Long Way in Biology”[Wolstencroft et al., 2005]


<div id="event-info-where" class="info-wh-info vcard"> <h2><a rel="bookmark" class="fn org location" href="/venues/V0-001-000693919-2"> Circus Krone Munich</a></h2> <div class="adr"> <span class="street-address">1</span><br> <span class="locality">Munich</span>, <span class="region">Bayern</span> <br> <span class="country-name">Germany</span>

Introduction Light Example: Publishing Semantic Mark-up

A firefox plug-in such as Operator can extract those semantic mark-up from the page and offers actions such as “add the event to your calendar” https://addons.mozilla.org/en-US/firefox/addon/4106

34


Introduction Example: Automatic Semantic mark-up

Normal page

Page Augmented

with Gnosis

Firefox Extension

Download Gnosis at https://addons.mozilla.org/de/firefox/addon/3999


Introduction Example: BBC’s Artist as Linked Data

<?xml version="1.0" encoding="utf-8"?> <rdf:RDF xmlns:rdf = "http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs = "http://www.w3.org/2000/01/rdf-schema#" xmlns:owl = "http://www.w3.org/2002/07/owl#" xmlns:dc = "http://purl.org/dc/elements/1.1/" xmlns:foaf = "http://xmlns.com/foaf/0.1/" xmlns:rel = "http://www.perceive.net/schemas/relationship/" xmlns:mo = "http://purl.org/ontology/mo/" xmlns:rev = "http://purl.org/stuff/rev#" > <rdf:Description rdf:about="/music/artists/a3cb23fc-acd3-4ce0-

8f36-1e5aa6a18432.rdf"> <rdfs:label>Description of the artist U2</rdfs:label> <foaf:primaryTopic rdf:resource="/music/artists/a3cb23fc-acd3-

4ce0-8f36-1e5aa6a18432#artist"/> </rdf:Description> <mo:MusicGroup rdf:about="/music/artists/a3cb23fc-acd3-4ce0-

8f36-1e5aa6a18432#artist"> <foaf:name>U2</foaf:name> <owl:sameAs rdf:resource="http://dbpedia.org/resource/U2" /> <foaf:page rdf:resource="/music/artists/a3cb23fc-acd3-4ce0-8f36-

1e5aa6a18432.html" /> <mo:musicbrainz

rdf:resource="http://musicbrainz.org/artist/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432.html" />

<mo:homepage rdf:resource="http://www.u2.com/" /> <mo:fanpage rdf:resource="http://www.atu2.com/" /> <mo:wikipedia rdf:resource="http://en.wikipedia.org/wiki/U2" /> <mo:imdb

rdf:resource="http://www.imdb.com/name/nm1277752/" /> <mo:myspace rdf:resource="http://www.myspace.com/u2" /> <mo:member rdf:resource="/music/artists/7f347782-eb14-40c3-

98e2-17b6e1bfe56c#artist" /> <mo:member rdf:resource="/music/artists/1f52af22-0207-40ac-

9a15-e5052bb670c2#artist" />

HTML: http://www.bbc.co.uk/music/artists/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432

RDF : http://www.bbc.co.uk/music/artists/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432.rdf


Introduction Complex Example: Linking Open Data Project

Goal: extend the Web with data commons by publishing open data sets using Semantic Web techs

Visit http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData !

Project Chartres

• RDFizers and ConverterToRdf

• Publishing Tools

• Semantic Web Browsers and Client Libraries

• Semantic Web Search Engines

• Applications

• […]

37


Introduction New York Times Thesaurus

For more than 150 years, The New York Times has meticulously indexed its archives. Through this process, we have developed an enormous collection of subject headings […].

Unfortunately, our list of subject headings is an island. For example, even though we can show you every article written about “Colbert, Stephen,” our databases can’t tell you that he was born on May 13, 1964, or that he lost the 2008 Grammy for best spoken word album to Al Gore. To do this we would need to map our subject headings onto other Web databases such as Freebase and DBPedia.

So that’s exactly what we did. Over the last several months we have manually mapped more than 5,000 person name subject headings onto Freebase and DBPedia. […]

So now you can visit http://data.nytimes.com/N66220017142656459133 and see that our “Colbert, Stephen” is equivalent to DBPedia’s http://dbpedia.org/resource/Stephen_Colbert and Freebase’s http://rdf.freebase.com/rdf/en.stephen_colbert. Even more importantly, your computer can visit http://data.nytimes.com/N66220017142656459133.rdf and get all of this information in a computer-readable (RDF) document.

October 29, 2009, 4:07 pmFirst 5,000 Tags Released to the Linked Data Cloud

By EVAN SANDHAUS AND ROB LARSON


Introduction Browsing the LOD with http://sig.ma/

39

Try

it!

http

://s

ig.m

a/se

arch

?q=

Pro

pran

olol


Introduction The new era of Semantic Apps

One of the highlights of October's Web 2.0 Summit in San Francisco was the emergence of 'Semantic Apps' as a force.

The purpose of this post is to highlight 10 Semantic Apps. […] It reflects the nascent status of this sector, even though people like Hillis and Spivack have been working on their apps for years now.

Read out more at http://www.readwriteweb.com/archives/10_semantic_apps_to_watch.php


Analizziamo il Semantic Web

idea

microfenomeno

macrofenomeno

problemi

anal

izza

re

creare

complessità

soluzione tecnica

soluzione sociale

innovare

Come gestire i dati sul Web?

KR + Web

Semantic Web

RDFSPARQL

SKOSOWL RIF

LOD

?



Credits

Introduction and RDF slides are partially based on “Fundamentals of the Semantic Web” by David Boothhttp://www.w3.org/2002/Talks/0813-semweb-dbooth/

42

Applied Semantic WebTimely. Practical. Reliable.http://applied-semantic-web.org

Semantic WebAn IntroductionEmanuele Della [email protected]://emanueledellavalle.org

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Semantic Web, an introduction

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