Ingegneria della conoscenza 2007-08
Emanuele Della Valle
Scienze e Tecniche Della Comunicazione
Parte V: conclusione
1. Semantic Web
Modellare e Condividere per Innovare
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 Un modello per studiare l’innovazione
 Il Semantic Web
 Esempi di applicazione
Sommario
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Innovazione
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Innovazione
3
creare
idea
macro
micro
analizzare
innovare
problemi
fenomeno
fenomeno
complessità = 6.000.000.000 persone
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Innovazione
4
creare
idea
macro
micro
analizzare
innovare
problemi
fenomeno
fenomeno
complessità
= magia
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Innovazione
5
creare
idea
macro
micro
analizzare
innovare
problemi
fenomeno
fenomeno
complessità
= magia
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Innovare …
6
creare
idea
innovare
micro
fenomeno
complessità
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… non è mai solo una questione di tecnologia
creare
idea
sociale
innovare
soluzione
micro
soluzione
tecnica
fenomeno
complessità
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Un modello per studiare l’innovazione
creare
idea
soluzione
sociale
macro
innovare
analizzare
problemi
micro
fenomeno
fenomeno
complessità
soluzione
tecnica
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Analizziamo il Web delle origini
9
Non riesco ad accedere
all’informazione
Ipertesti + Internet
creare
problemi
Come posso
scrivere?
soluzione
sociale
macro
Condividere info
Link a cose
interessanti
fenomeno
Esplosione del
fenomeno Web
innovare
analizzare
Come trovo
le pagine?
idea
soluzione
tecnica
micro
fenomeno
WWW
complessità
URI
HTTP
HTML
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Analizziamo google
10
Come trovo
le pagine?
creare
problemi
idea
soluzione
sociale
macro
Condividere info
Link a cose
interessanti
fenomeno
Il fenomeno
Google
innovare
analizzare
Google
spoofing
Indici + SVM
Page
Rank
soluzione
tecnica
micro
fenomeno
Google
complessità
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Analizziamo il Web 2.0
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Come posso
scrivere?
wiki-wiki e diari Web
creare
idea
Come gestire
tutta questa
info?
soluzione
sociale
macro
Condividere info
Link a cose
interessanti
fenomeno
I fenomeni
Wikipedia,
blogosphere,
…
innovare
analizzare
problemi
wiki
blog
soluzione
tecnica
micro
fenomeno
Web 2.0
complessità
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Analizziamo il Semantic Web
12
Come gestire i
dati sul Web?
creare
problemi
idea
soluzione
sociale
macro
Condividere info
Link a cose
interessanti
fenomeno
?
innovare
analizzare
?
KR + Web
Modellare
RDF OWL
SPARQL
RIF
soluzione
tecnica
micro
fenomeno
complessità
Semantic
Web
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Semantic Web
 Un modo di specificare dati e relazioni tra i dati
 Permette di condividere e riusare dati tra applicazioni,
imprese e gruppi di interesse
 Una collezione di tecnologie
 RDF
 RDF-S
 OWL
 GRDDL
 SPARQL
 …
 La prossima onda del Web da surfare …
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Tim Berners-Lee’s Semantic Wave (2003)
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Tim Berners-Lee’s Semantic Wave (2008)
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The “corporate” landscape is moving
 Major companies offer (or will offer) Semantic Web
tools or systems using Semantic Web:
 Adobe, Oracle, IBM, HP, Software AG, GE, Northrop
Gruman, Altova, Microsoft, Dow Jones, …
 Others are using it (or consider using it) as part of their
own operations:
 Novartis, Boeing, Pfizer, Telefónica, …
 Some of the names of active participants in W3C SW
related groups:
 ILOG, HP, Agfa, SRI International, Fair Isaac Corp.,
Oracle, Boeing, IBM, Chevron, Siemens, Nokia,
Pfizer, Sun, Eli Lilly, …
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The 2007 Gartner predictions
 During the next 10 years, Web-based technologies will
improve the ability to embed semantic structures [… it]
will occur in multiple evolutionary steps…
 By 2017, we expect the vision of the Semantic Web […]
to coalesce […] and the majority of Web pages are
decorated with some form of semantic hypertext.
 By 2012, 80% of public Web sites will use some level
of semantic hypertext to create SW documents […]
15% of public Web sites will use more extensive
Semantic Web-based ontologies to create semantic
databases
Source: “Finding and Exploiting Value
in Semantic Web Technologies on the Web”,
Gartner Research Report, May 2007
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The Web Today
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Large number of integrations
- ad hoc
- pair-wise
Millions of Applications
Too much information
to browse, need for
searching and mashing
up automatically
10100
10
0010
01
101
0
101
01
1101
110
1
10
1
10
0
1
1 0
1 0
1 0
0
1 1
0
1 1
1
10 0
1 101
0
1
010
0
1
1
0
Each site is “understandable” for us
Search &
Mash-up
Engine
Computers don’t “understand” much
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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”
[ source http://www.thefarside.com/ ]

" 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
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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/
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Two ways for computer to “understand” 1/2
 Smarter machines
 Smarter data
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Two ways for computer to “understand” 2/2
 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 combinig them
 E.g., NLP meets IP
– NLP: What does your eye see?
– IP: I see a sea
– NLP: You see a “c”?
Some NLP Related Entertainment
http://www.cl.cam.ac.uk/Research/
– IP: Yes, what else could it be?
NL/amusement.html
 Not the Semantic Web approach
 Smarter Data
 Make data easier for machines to publish, share, find and
understand
 E.g. wornet2.1:sea/noun/1 vs. wordnet2.1:c/noun/10
 The Semantic Web approach
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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
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The Semantic Web 2/4
 “The Semantic Web is not a separate Web,
but an extension of the current one […] ”
Web 1.0
The Web Today
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The Semantic Web 3/4
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 “The Semantic Web […] , in which information is
given well-defined meaning […]”
Web 1.0
Semantic Web
?
Human understandable but
“only” machine-readable
Human and machine
“understandable”
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The Semantic Web 4/4
27
Fewer Integration
- standard
- multi-lateral
[…] better enabling
computers and people to
work in cooperation.
Even More Applications
T
ME
T
ME
A
T
ME
A
A
Semantic Web
T
ME
A
T
ME
Easier to understand for people
A
Semantic
Mash-ups
&
Search
More “understandable” for computers
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Semantic Web “layer cake”
Already
Possible
Under
Investigation
Standardized
[ source http://www.w3.org/2007/03/layerCake.png ]
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Data Interchange: RDF
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RDF: Resource Description Framework
 RDF is a general method for conceptual description or
modeling of information that is implemented in web
resources
 Basically speaking, the RDF data model is based upon
the idea of making statements about Web resources, in
the form of subject-predicate-object expressions.These
expressions are known as triples in RDF terminology.
 The subject denotes the resource, and the predicate
denotes traits or aspects of the resource and expresses
a relationship between the subject and the object.
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RDF: Resource Description Framework
 For example, one way to represent the notion "The sky
has the color blue" in RDF is as the triple:
 a subject denoting "the sky"
 wordnet:synset-sky-noun-1
 a predicate denoting "has the color"
Click &
 wordnet:wordsense-color-verb-6
read!
 an object denoting "blue“
 wordnet:synset-blue-noun-1
 In FOL we could write
 predicate(subject, object)

wn:wordsense-color-verb-6(wn:synset-sky-noun-1, wn:synset-blue-noun-1)
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Serialization of RDF
 Serialization (N3 notation)
 subject predicate object .
@prefix wn: <http://www.w3.org/2006/03/wn/wn20/schema/>.
wn:synset-sky-noun-1 wn:wordsense-color-verb-6 wn:synset-blue-noun-1 .
 Serialization (N3 notation)
 <rdf:Description about="subject">
<predicate rdf:resource="object“/>
</rdf:Description>
< rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:wn="http://www.w3.org/2006/03/wn/wn20/schema/" >
<rdf:Description about="wn:synset-sky-noun-1">
<wn:wordsense-color-verb-6
rdf:resource="wn:synset-blue-noun-1"/>
</rdf:Description>
</rdf:RDF>
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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-acd34ce0-8f36-1e5aa6a18432.rdf">
<rdfs:label>Description of the artist U2</rdfs:label>
<foaf:primaryTopic rdf:resource="/music/artists/a3cb23fcacd3-4ce0-8f36-1e5aa6a18432#artist"/>
</rdf:Description>
<mo:MusicGroup rdf:about="/music/artists/a3cb23fc-acd34ce0-8f36-1e5aa6a18432#artist">
<foaf:name>U2</foaf:name>
<owl:sameAs rdf:resource="http://dbpedia.org/resource/U2"
/>
<foaf:page rdf:resource="/music/artists/a3cb23fc-acd3-4ce08f36-1e5aa6a18432.html" />
<mo:musicbrainz
rdf:resource="http://musicbrainz.org/artist/a3cb23fc-acd34ce0-8f36-1e5aa6a18432.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-eb1440c3-98e2-17b6e1bfe56c#artist" />
<mo:member rdf:resource="/music/artists/1f52af22-020740ac-9a15-e5052bb670c2#artist" />
http://www.bbc.co.uk/music/artists/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432
HTML:
RDF : http://www.bbc.co.uk/music/artists/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432.rdf
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If you want to see the triples
 RDF is not always serialized in N3 notation, so if you
want to see the triples you can use W3C RDF Validation
Service
 http://www.w3.org/RDF/Validator/
 To see the triples in the RDF version of the page about
U2 on BCC
 http://www.w3.org/RDF/Validator/ARPServlet?URI=
http%3A%2F%2Fwww.bbc.co.uk%2Fmusic%2Fartis
ts%2Fa3cb23fc-acd3-4ce0-8f361e5aa6a18432.rdf+&PARSE=Parse+URI%3A+&TRI
PLES_AND_GRAPH=PRINT_TRIPLES&FORMAT=PNG
_EMBED
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Query: SPARQL
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What is SPARQL?
 SPARQL
 is the query language of the Semantic Web
 stays for SPARQL Protocol and RDF Query
Language
 A Query Language ...:
Find names and websites of contributors to PlanetRDF:
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name ?website
FROM <http://planetrdf.com/bloggers.rdf>
WHERE { ?person foaf:weblog ?website ;
?person foaf:name ?name .
?website a foaf:Document }
 ... and a Protocol.
http://.../qps?
query-lang=http://www.w3.org/TR/rdf-sparql-query/ &graphid=http://planetrdf.com/bloggers.rdf &query=PREFIX foaf:
<http://xmlns.com/foaf/0.1/...
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Ontology: RDF-S and OWL
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What does it mean?
38
Formal, explicit specification of a shared conceptualization
Machine
readable
It makes
domain
assumption
explicit
A conceptual
model of some
aspects of the
reality
Several people
agrees that such
conceptual model
is adequate to
describe such
aspects of the
reality
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How much explicit shall the specification be?
“A little semantics, goes
a long way”
[James Hendler, 2001]
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A simple ontology
40
creates
Artist
Piece
Painter
Paint
paints
Sculptor
Sculpt
sculpts
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Specifying classes, sub-classes and instances
 Creating a class
 RDFS: Artist rdf:type rdfs:Class .
 FOL: x Artist(x)
Artist
Painter
Sculptor
Rodin
 Creating a subclass
 RDFS: Painter rdfs:subClassOf Artist .
 RDFS: Sculptor rdfs:subClassOf Artist .
 FOL: x [Painter(x)  Sculptor(x)  Artist(x)]
 Creating an instance
 RDFS: Rodin rdf:type Sculptor .
 FOL: Sculptor(Rodin)
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Specifying properties and sub-properties
 Creating a property
 RDFS: creates rdf:type rdf:Property .
 FOL: x y Creates(x,y)
 Using a property
 RDFS: Rodin creates TheKiss .
 FOL: Creates(Rodin, TheKiss)
 Creating subproperties
 RDFS: paints rdfs:subPropertyOf creates .
 FOL: x y [Paints(x,y)  Creates(x,y)]
 RDFS: sculpts rdfs:subPropertyOf creates .
 FOL: x y [Sculpts(x,y)  Creates(x,y)]
creates
paints
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Specifying domain/range constrains
 Checking which classes and properties can be use
together
 RDFS:
creates rdfs:domain Artist .
creates rdfs:range Piece .
paints rdfs:domain Painter .
paints rdfs:range Paint .
sculpts rdfs:domain Sculptor .
sculpts rdfs:range Sculpt .
 FOL:
x y [Creates(x,y)  Artist(x)  Piece(y)]
x y [Paints(x,y)  Painter(x)  Paint(y)]
x y [Sculpts(x,y)  Sculptor(x)  Sculpt(y)]
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The ontology we specified
44
creates
Artist
Piece
Painter
Paint
paints
Sculptor
Sculpt
sculpts
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RDF semantics (a part of it)
hypothesis
x rdfs:subClassOf y .
conclusion
a rdf:type y .
a rdf:type x .
x rdfs:subClassOf y .
x rdfs:subClassOf z .
y rdfs:subClassOf z .
x a y .
x b y .
a rdfs:subPropertyOf b .
a rdfs:subPropertyOf b .
a rdfs:subPropertyOf c .
b rdfs:subPropertyOf c .
x a y .
x rdf:type z .
a rdfs:domain z .
x a u .
u rdf:type z .
a rdfs:range z .
Read out more in RDF Semantics http://www.w3.org/TR/rdf-mt/
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First Order Calculus and RDF semantics
 RDFS inference rules are valid deduction
hypothesis
Conclusion
p rdfs:subClassOf q .
a rdf:type q .
a rdf:type p .
 In FOL
x [ P(x)  Q(x)],
P(A)
 Q(A)
 We can demonstate that it is a valid deduction using
First Order Calculus
1. x [P(x)  Q(x)]
hypothesis
2. P(A)
hypothesis
3. P(A)  Q(A)
E(1)
4. Q(A)
E(3,2)
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Without Inference
47
 A recipient, that only understands XML syntax,
 receiving
<RDF>
<Description about="Rodin">
<sculpts resource="TheKiss"/>
</Description>
</RDF>
 can answer the following queries
 What does Rodin sculpt?
RDF/Description[@about='Rodin']/sculpts/@resource
 Who does sculpt TheKiss?
RDF/Description[sculpts/@resource='TheKiss']/@about
 Try out your self at http://www.mizar.dk/XPath/
 but it cannot answer
 Who is Rodin?
 What is TheKiss?
 Is there any Sculptor/Scupts?
 Is there any Artist/Piece?
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Knowing the ontology and RDF semantics …
 A recipient, that knows the ontology and “understands”
RDF semantics,
creates
Artist
Piece
Painter
Paint
paints
Sculptor
Rodin
 Receiving
Sculpt
sculpts
Rodin sculpts TheKiss .
TheKiss
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… a reasoner can answer 1/2
49
 the previous queries
 What does Rodin sculpt?
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX ex:
<http://www.ex.org/schema#>
SELECT ?x
WHERE { ex:Rodin ex:sculpts ?x }
?x = ex:TheKiss
 Who does sculpt TheKiss?
WHERE { ex:Rodin ex:sculpts ?x }
?x = ex:Rodin
 and it can also answer
 Who is Rodin?
WHERE { ex:Rodin a ?x }
?x = ex:Artist, ex:Sculptor, rdfs:Resource
 What is TheKiss?
WHERE { ex:TheKiss a ?x }
?x = ex:Sclupt, ex:Piece, rdfs:Resource
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… a reasoner can answer 2/2
50
 Is there any Sculptor?
WHERE { ?x a ex:Sculptor}
?x = ex:Rodin
 Is the any Artist?
WHERE { ?x a ex:Artist }
?x = ex:Rodin
 Is there any Sculpt?
WHERE { ?x a ex:Sculpt }
?x = ex:TheKiss
 Is there any Piece?
WHERE { ?x a ex:Piece }
?x = ex:TheKiss
 Is there any Paint?
WHERE { ?x a ex:Paint }
0 results
 Is there any Painter?
WHERE { ?x a ex:Painter }
0 results
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SPARQL vs Reasoner
51
 SPARQL alone cannot answer queries that require
reasoning
SPARQL
service
RDF
 but a reasoner can be exposed as a SPARQL service.
RDF
Reasoner
SPARQL
service
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More expressive power 1/3
 RDFS is a light ontological language that allows for defining
simple vocabularies.
 One may want also express
 Cardinality constrains (max, min, exactly) for properties
usage
 Es. a Polygon has 3 or more edges
 x [Polygon(x)  ≥3y Edge(y)  Forms(y,x) ]
 Property types
 transitive
– e.g. hasAncestor is a transitive property: if A
hasAncestor B and B hasAncestor C, then A
hasAncestor C.
– x y z [HasAncestor(x,y) 
HasAncestor(y,z)  HasAncestor(x,z) ]
 inverse
– e.g. sclupts has isSculptedBy as inverse
property:
if A sclupts B then B isSculptedBy A
– x y [Sculpts(x,y)  IsSculptedBy(y,x) ]
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More expressive power 2/3
53
simmetric
– e.g. isCloseTo is a simmetric property:
if A isCloseTo B then B isCloseTo A
– x y [IsCloseTo(x,y)  IsCloseTo(y,x) ]
 Restrictions of usage for a specific property
 All values of property must be of a certain kind
– e.g. a D.O.C. Wine can be only produced by a
Certified Wienery
– x y [DOCWine(x)  Produces(x,y) 
CertifiedWienery(y)]
 Some values of property must be of a certain kind
– e.g. a Famous Painter must have painted some
Famous Painting
– x [FamousPainter(x)  y FamousPaint(y) 
IsPaintedBy(y,x)]
 A class is defined combining other classes (union,
intersection, negation, ...)
 A white wine is a Wine and its color is “white”
 x [Wine(x)  White(x)]

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More expressive power 3/3
54
 Two instances refers to the same real object


“The Boss” and “Bruce Springsteen” are two
names for the same person
TheBoss = BruceSpringsteen
 Two classes refers to the same set


“Painters” in english and “Pittori” in italian
x [Painter(x)  Pittore(x)]
 Two properties refers to the same binary
relationship
 “Paints” in english and “Dipinge” in italian
 x y [Paints(x,y)  Dipinge(x,y)]
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Expressivity vs. Tractability
 The more an ontological language is expressive the
less is tractable
 the Web Ontology Language (OWL) comes with several
profiles that offers different trade-offs between
expressivity and tractability.
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OWL 2 profiles
 OWL 1 defines only one fragment (OWL Lite)
 And it isn’t very tractable!
 OWL 2 defines several different fragments with
 Useful computational properties
 E.g., reasoning complexity in range LOGSPACE to
PTIME
 Useful implementation possibilities
 E.g., Smaller fragments implementable using
RDBs
 OWL 2 profiles
 OWL 2 EL, OWL 2 QL, OWL 2 RL
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OWL 2 EL
 Useful for applications employing ontologies that
contain very
 large number of properties and/or classes
 Captures expressive power used by many largescaleontologies E.g.; SNOMED CT, NCI thesaurus
 Features
 Included: existential restrictions, intersection,
subClass,equivalentClass, disjointness, range and
domain, object property inclusion possibly involving
property chains, and data property inclusion,
transitive properties, keys …
 Missing: include value restrictions, Cardinality
restrictions (min, max and exact), disjunction and
negation
 Maximal language for which reasoning (including query
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OWL 2 QL
 Useful for applications that use very large volumes of
data, and where query answering is the most important task
 Captures expressive power of simple ontologies like thesauri,
classifications, and (most of) expressive power of ER/UML
schemas
 E.g., CIM10, Thesaurus of Nephrology, ...
 Features
 Included: limited form of existential restrictions,
subClass, equivalentClass, disjointness, range & domain,
symmetric properties, …
 Missing: existential quantification to a class, self
restriction, nominals, universal quantification to a class,
disjunction etc.
 Can be implemented on top of standard relational DBMS
 Maximal language for which reasoning (including query
answering) is known to be worst case logspace (same as
DB)
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OWL 2 RL
 Useful for applications that require scalable reasoning
without sacrifying too much expressive power, and
where query answering is the most important task
 Support most OWL features but
 with restrictions placed on the syntax of OWL 2
 standard semantics only apply when they are used
in a restricted way
 Can be implemented on top of rule extended DBMS
 E.g., Oracle’s OWL Prime implemented using
forward chaining rules in Oracle 11g
 Related to DLP and pD*
 Allows for scalable (polynomial) reasoning using
rule-based technologies
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Application
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Light weight semantic mark-up
<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>
 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
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Linking Open Data Project
 Goal: extend the Web with data commons by
publishing open data sets using Semantic Web techs
Project Chartres
• RDFizers and
ConverterToRdf
• Publishing Tools
• Semantic Web
Browsers and
Client Libraries
• Semantic Web
Search Engines
• Applications
• […]
Visit http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData !
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Navigating the Semantic Web
 Use a Semantic Web search engine to enter into it
 E.g., sindice http://sindice.com/
 Search for something (e.g., Varese)
 Click and browse
 NOTE: It’s meant for machine consumption!
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The new era of Semantic Apps
64
 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
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Esempi di applicazioni
 Allen Brain Atlas Gene Expression Results
 http://sw.neurocommons.org/hcls_gene_image.html
 SWEO’s use case collection
 http://www.w3.org/2001/sw/sweo/public/UseCases/
 Linking Open Data Project
 http://esw.w3.org/topic/SweoIG/TaskForces/Community
Projects/LinkingOpenData
 Music Event Explorer
 http://meex.cefriel.it/meex/
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Music Event Explorer
 Esigenza: dove posso andare a sentire musica folk nei
prossimi giorni?
 Soluzione manuale:
1. Vado su musicmoz e scopro i cantanti che fanno
musica folk
2. Vado su musicbrainz e guardo quali album hanno
pubblicato
3. Per ciascuno di quelli che mi piace cerco su EVDB
se ci ha organizzato eventi nei prossimi giorni
4. Mi appunto i posti e poi li cerco in GoogleMaps
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Soluzione manuale
1. Vado su musicmoz e scopro i cantanti che fanno musica folk
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Soluzione manuale
2. Vado su musicbrainz e guardo quali album hanno pubblicato
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Soluzione manuale
3. Per ciascuno di quelli che mi piace cerco su EVDB se ci ha
organizzato eventi nei prossimi giorni
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Soluzione manuale
4. Mi appunto i posti e poi li cerco in GoogleMaps
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Music Event Explorer
 Una soluzione poco praticabile …
 … ma automatizzabile
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http://meex.cefriel.it/meex