Post on 15-Jan-2016
description
Ingegneria della conoscenza 2007-08Emanuele Della ValleScienze e Tecniche Della Comunicazione
1. Semantic WebModellare e Condividere per Innovare
Parte V: conclusione
I-1
2Sommario
Un modello per studiare l’innovazione
Il Semantic Web
Esempi di applicazione
I-1Innovazione
3
I-1Innovazione
4
idea
microfenomeno
macrofenomeno
problemi
inn
ovare
an
aliz
zare
creare
complessità = 6.000.000.000 persone
I-1Innovazione
5
idea
microfenomeno
macrofenomeno
problemi
inn
ovare
an
aliz
zare
creare
complessità = magia
I-1Innovazione
6
idea
microfenomeno
macrofenomeno
problemi
inn
ovare
an
aliz
zare
creare
complessità = magia
scie
nza
ingegneria
I-1Innovare …
7
idea
microfenomeno
inn
ovare
creare
complessità
I-1… non è mai solo una questione di tecnologia
8
idea
microfenomeno
inn
ovare
soluzione tecnic
a
soluzione sociale
creare
complessità
I-1Un modello per studiare l’innovazione
9
idea
microfenomeno
macrofenomeno
problemi
an
aliz
zare
creare
complessità
soluzione tecnic
a
soluzione sociale
inn
ovare
I-1Analizziamo il Web delle origini
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idea
microfenomeno
macrofenomeno
problemi
an
aliz
zare
creare
complessità
soluzione tecnic
a
soluzione sociale
inn
ovare
Non riesco ad accedere all’informazione Ipertesti + Internet
WWW
Condividere infoLink a cose interessanti
URI HTTP HTML
Esplosione del fenomeno Web
Come trovole pagine?
Come posso scrivere?
I-1Analizziamo google
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idea
microfenomeno
macrofenomeno
problemi
an
aliz
zare
creare
complessità
soluzione tecnic
a
soluzione sociale
inn
ovare
Come trovole pagine?
Indici + SVM
PageRank
Il fenomeno Google
Google spoofing
Condividere infoLink a cose interessanti
I-1Analizziamo il Web 2.0
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idea
microfenomeno
macrofenomeno
problemi
an
aliz
zare
creare
complessità
soluzione tecnic
a
soluzione sociale
inn
ovare
Come posso scrivere?
wiki-wiki e diari Web
Web 2.0
wiki blog
I fenomeni Wikipedia, blogosphere, …
Come gestire tutta questa info?
Condividere infoLink a cose interessanti
I-1Analizziamo il Semantic Web
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idea
microfenomeno
macrofenomeno
problemi
an
aliz
zare
creare
complessità
soluzione tecnic
a
soluzione sociale
inn
ovare
Come gestire i dati sul Web?
KR + Web
Semantic Web
ModellareRDF OWL
SPARQL RIF
?
?
Condividere infoLink a cose interessanti
I-1
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I-1Semantic 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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I-1Tim Berners-Lee’s Semantic Wave (2003)
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I-1Tim Berners-Lee’s Semantic Wave (2008)
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I-1The “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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I-1The 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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I-1The Web Today
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
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I-1What 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
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[ source http://www.thefarside.com/ ]
I-1What 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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I-1Two ways for computer to “understand” 1/2
Smarter machines
Smarter data
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I-1Two 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”?– 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. wornet2.1:sea/noun/1 vs. wordnet2.1:c/noun/10 The Semantic Web approach
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Some NLP Related Entertainment http://www.cl.cam.ac.uk/Research/
NL/amusement.html
I-1The 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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I-1The Semantic Web 2/4
“The Semantic Web is not a separate Web, but an extension of the current one […] ”
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Web 1.0 The Web Today
I-1The Semantic Web 3/4
“The Semantic Web […] , in which information is given well-defined meaning […]”
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Human understandable but “only” machine-
readable
Human and machine “understandable”
?
Web 1.0 Semantic Web
I-1The Semantic Web 4/4
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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
I-1Semantic Web “layer cake”
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Standardized
UnderInvestigation
Already Possible
[ source http://www.w3.org/2007/03/layerCake.png ]
I-1Data Interchange: RDF
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I-1RDF: 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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I-1RDF: 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"
wordnet:wordsense-color-verb-6 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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Click & read!
I-1Serialization 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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I-1Example: 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" />
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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
I-1If 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%2Fartists%2Fa3cb23fc-acd3-4ce0-8f36-1e5aa6a18432.rdf+&PARSE=Parse+URI%3A+&TRIPLES_AND_GRAPH=PRINT_TRIPLES&FORMAT=PNG_EMBED
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I-1Query: SPARQL
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I-1What 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/ &graph-id=http://planetrdf.com/bloggers.rdf &query=PREFIX foaf: <http://xmlns.com/foaf/0.1/...
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I-1Ontology: RDF-S and OWL
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I-1What does it mean?
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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
I-1How much explicit shall the specification be?
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“A little semantics, goes a long way”[James Hendler, 2001]
“A little semantics, goes a long way”[James Hendler, 2001]
I-1A simple ontology
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Artist Piece
Painter Paint
paints
Sculptor Sculpt
sculpts
creates
I-1Specifying classes, sub-classes and instances
Creating a class RDFS: Artist rdf:type rdfs:Class . FOL: x Artist(x)
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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ArtistPainter
SculptorRodin
I-1
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)]
Specifying properties and sub-properties 43
creates
paints
I-1Specifying 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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I-1The ontology we specified
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Artist Piece
Painter Paint
paints
Sculptor Sculpt
sculpts
creates
I-1RDF semantics (a part of it)
hypothesis conclusion
x rdfs:subClassOf y . 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 .
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Read out more in RDF Semantics http://www.w3.org/TR/rdf-mt/
I-1 First Order Calculus and RDF semantics
RDFS inference rules are valid deductionhypothesis Conclusionp rdfs:subClassOf q . a rdf:type q .a rdf:type p .
In FOLx [ P(x) Q(x)],P(A) Q(A)
We can demonstate that it is a valid deduction using First Order Calculus1. 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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I-1Without Inference
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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I-1Knowing the ontology and RDF semantics …
A recipient, that knows the ontology and “understands” RDF semantics,
Receiving Rodin sculpts TheKiss .
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Artist Piece
Painter Paint
paints
Sculptor Sculpt
sculpts
creates
Rodin TheKiss
I-1… a reasoner can answer 1/2
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 ?xWHERE { 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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I-1… a reasoner can answer 2/2
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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I-1SPARQL vs Reasoner
SPARQL alone cannot answer queries that require reasoning
but a reasoner can be exposed as a SPARQL service.
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RDFSPARQLservice
ReasonerRDFSPARQLservice
I-1More 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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I-1More expressive power 2/3
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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I-1More expressive power 3/3
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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I-1Expressivity 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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I-1OWL 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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I-1OWL 2 EL
Useful for applications employing ontologies that contain very
large number of properties and/or classes Captures expressive power used by many large-
scaleontologies 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 answering) known to be worst-case polynomial
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I-1OWL 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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I-1OWL 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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I-1Application
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I-1Light weight 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
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<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>
I-1Linking Open Data Project
Goal: extend the Web with data commons by publishing open data sets using Semantic Web techs
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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
• […]
I-1Navigating 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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I-1The 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
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I-1Esempi 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/
CommunityProjects/LinkingOpenData
Music Event Explorer http://meex.cefriel.it/meex/
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I-1Music 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 folk2. 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 giorni4. Mi appunto i posti e poi li cerco in GoogleMaps
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I-1Soluzione manuale
1. Vado su musicmoz e scopro i cantanti che fanno musica folk
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I-1Soluzione manuale
2. Vado su musicbrainz e guardo quali album hanno pubblicato
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I-1Soluzione manuale
3. Per ciascuno di quelli che mi piace cerco su EVDB se ci ha organizzato eventi nei prossimi giorni
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I-1Soluzione manuale
4. Mi appunto i posti e poi li cerco in GoogleMaps
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I-1Music Event Explorer
Una soluzione poco praticabile …
… ma automatizzabile
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I-1http://meex.cefriel.it/meex
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