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: Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June, 2016 1/46

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Page 1: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

:

Semantic WebIntroduction to semantic data

Catherine Comparot Nathalie Hernandez Cassia Trojahn

UT2J - IRIT, MELODI Team

7th June, 2016

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Page 2: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Agenda

Why the Semantic Web ?

Semantic data on the Linked Open Data

One step further : the Semantic Web

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Page 3: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Agenda

Why the Semantic Web ?Artificial IntelligenceThe syntactic webLimits of the syntactic Web

Semantic data on the Linked Open Data

One step further : the Semantic Web

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Page 4: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Artificial Intelligence

A definition, proposed by J. Pitrat

”Artificial Intelligence is the science which aims is to makemachines do what humans are capable of doing.”

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Page 5: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Levels of ”understanding”

I Data : Result of a measure

I Information : Data and its context

I Knowledge : Possible deductions from information withbackground rules (reasoning)

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Page 6: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Levels of ”understanding”

I Data : Result of a measure

I Information : Data and its context

I Knowledge : Possible deductions from information withbackground rules (reasoning)

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Page 7: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Levels of ”understanding”

I Data : Result of a measure

I Information : Data and its context

I Knowledge : Possible deductions from information withbackground rules (reasoning)

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Page 8: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Levels of ”understanding”

I Data : Result of a measure

I Information : Data and its context

I Knowledge : Possible deductions from information withbackground rules (reasoning)

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Page 9: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Levels of ”understanding”

I Data : Result of a measure

I Information : Data and its context

I Knowledge : Possible deductions from information withbackground rules (reasoning)

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Page 10: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Logic

Logic models formalize common sense andreasoning mechanisms in order to studyand automatize them.

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Page 11: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What is a logic ?

Constituents of a logic

I A syntax (grammar + vocabulary)

I A semantic

I A mechanism for deduction

Their roles (respectively)

I Describe data

I Associate meaning to the data

I Deduce new knowledge

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Page 12: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Internet and the Web

InternetNetwork of computers offering countless services

The WebThe World Wide Web is an information systemsupported by Internet based on :

I a system addressing the resources (page,image, video) via their URL

I HTML pages related thanks to hyperlinks

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Page 13: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

The W3C

A organism standardizing the web

Founded in 1993 by Tim Berners Lee

What does the W3C standardize?

I Web Application (HTML, CSS, Ajax...)

I Web Architectures (URL, HTTP...)

I XML & consorts (XPath, ...)

I Web Services (SOAP, WSDL)

I Semantic Web languages (RDF, OWL, ..)

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Page 14: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What you can do with the Web...

I Search for plane tickets to go toVenice

I Adapt the dates according to forecastI Avoid rainy days until I receive the

boots that I’ve orderedI Take into consideration my medical

constraints

I Book a room in the Hotel where G.Clooney stayed

I Command a pizza without gluten anda Torus bottle to cheer me up as noroom is available at the suitabledates...

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Page 15: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What you can do with the Web...

I Search for plane tickets to go toVenice

I Adapt the dates according to forecastI Avoid rainy days until I receive the

boots that I’ve orderedI Take into consideration my medical

constraints

I Book a room in the Hotel where G.Clooney stayed

I Command a pizza without gluten anda Torus bottle to cheer me up as noroom is available at the suitabledates...

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Page 16: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What you can do with the Web...

I Search for plane tickets to go toVenice

I Adapt the dates according to forecastI Avoid rainy days until I receive the

boots that I’ve orderedI Take into consideration my medical

constraints

I Book a room in the Hotel where G.Clooney stayed

I Command a pizza without gluten anda Torus bottle to cheer me up as noroom is available at the suitabledates...

10/46

Page 17: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What you can do with the Web...

I Search for plane tickets to go toVenice

I Adapt the dates according to forecastI Avoid rainy days until I receive the

boots that I’ve orderedI Take into consideration my medical

constraints

I Book a room in the Hotel where G.Clooney stayed

I Command a pizza without gluten anda Torus bottle to cheer me up as noroom is available at the suitabledates...

10/46

Page 18: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What you can do with the Web...

I Search for plane tickets to go toVenice

I Adapt the dates according to forecastI Avoid rainy days until I receive the

boots that I’ve orderedI Take into consideration my medical

constraints

I Book a room in the Hotel where G.Clooney stayed

I Command a pizza without gluten anda Torus bottle to cheer me up as noroom is available at the suitabledates...

10/46

Page 19: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What you can do with the Web...

I Search for plane tickets to go toVenice

I Adapt the dates according to forecastI Avoid rainy days until I receive the

boots that I’ve orderedI Take into consideration my medical

constraints

I Book a room in the Hotel where G.Clooney stayed

I Command a pizza without gluten anda Torus bottle to cheer me up as noroom is available at the suitabledates...

10/46

Page 20: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What you can do with the Web...

I Search for plane tickets to go toVenice

I Adapt the dates according to forecastI Avoid rainy days until I receive the

boots that I’ve orderedI Take into consideration my medical

constraints

I Book a room in the Hotel where G.Clooney stayed

I Command a pizza without gluten anda Torus bottle to cheer me up as noroom is available at the suitabledates...

... if you know where to look ...

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Page 21: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What you do not want to do...

:(

I Use complicated tools

I Search in several sources

I Go further than the first google page of results ...

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Page 22: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Any semantics in this Web to help?

What humans see

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Page 23: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Any semantics in this Web to help?

What computers see ...

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Page 24: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Any semantics in this Web to help?

What computers see?

More syntax than dogs thanks to HTML !

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Page 25: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Any semantics in this Web to help?

Limitations of HTML markups

I HTML markups are defined by web page authors and notpeople looking for information

I Markups indicate information ”zones” and links betweenresources

I No formal semantic or common sense associated to thesezones or links

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Page 26: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Any semantics in this Web to help?

The data used in Geosciences

I data distributed in several sources

I data in different formats

I a considerable effort to use (extract, combine, ...) them forspecific applications

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Page 27: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Semantics please HELP US!

The founder article of the Semantic Web

I Tim Berners Lee, at the conference WWW in 1994http://www.w3.org/Talks/WWW94Tim/

I The Semantic Web is a new form of the syntactic Web inwhich content is meaningful to computers and humansthus unleashing a revolution of new possibilities

I The Semantic Web relies on standards allowing torepresent knowledge usable by computers.

I Today’s reality : the Linked Open Data (LOD)

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Page 28: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Agenda

Why the Semantic Web ?

Semantic data on the Linked Open DataData semantizationLinked Open Data cloud

One step further : the Semantic Web

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Page 29: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Sacre Tim !

From 1 To 5 stars data !Tim Berners Lee (him again!!!) has proposed a hierarchisation ofdata which reflects their quality in the perspective of making themopen and reusable.

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Page 30: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

One star data

data characteristics

I Data accessible on the Web, with an open license.For example, the following pdf document :

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Page 31: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Two star data

data characteristics

I data in a structured format.For example, the same table but in the xls format.

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Page 32: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Three star data

data characteristics

I data in an open format.For example, the same table in csv.

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Page 33: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Four star data

data characteristics

I data described with RDF (a W3C formalism!)

I First step towards semantic description of data !!!For example, the same table in RDF.

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Page 34: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

The RDF, graph language

Bases of RDF : the triplet

I A triplet is composed of 3 resources :I The subject : the resource the representation is talking aboutI The predicate : the property of the subjectI The object : who/what the property links the subject to

What RDF adds

I Resources can be physical documents (web pages, video...),”moral” resources (person, institution, color,...) or data value

I The predicate makes explicit the nature of the link betweenresources

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Page 35: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

The RDF, graph language

Bases of RDF : the triplet

I A triplet is composed of 3 resources :I The subject : the resource the representation is talking aboutI The predicate : the property of the subjectI The object : who/what the property links the subject to

What RDF adds

I Resources can be physical documents (web pages, video...),”moral” resources (person, institution, color,...) or data value

I The predicate makes explicit the nature of the link betweenresources

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Page 36: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

The IRI

I Unique identification of resources on the Web

I Evolution of the notion of URL for internationalizationI Examples :

I IRI of an observation :http://pelican/adreamdataCNRS.RDC.EXT.LUX.mesure 01/03/2015%2000

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Page 37: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Examples of RDF triplets

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Page 38: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Five star data

data characteristics

I data linked to data from other data sets

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Page 39: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

The Linked Open Data cloud

Where are we now ?

I Who? A lot of people on the LOD : BBC, MusicBrainz,Wikipedia, IBM, IEEE, flickr, etc...

I What? Everything : 5 star data dealing with music, science,geography, government, ...

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Page 40: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Agenda

Why the Semantic Web ?

Semantic data on the Linked Open Data

One step further : the Semantic WebInterrogation de donnees liees : SPARQLOntologyReasoning

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Page 41: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

More semantics thanks to W3C standards !

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Page 42: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Querying RDF data with SPARQL

What is SPARQL ?

I Retrieves, deletes, updates RDF graphs

I Works by indicating graph patterns

I Syntax similar to SQL

I Not suited for end-users...

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Page 43: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Example of SPARQL query

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Page 44: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What is an ontology ?

An academic definitionAn ontology is a formal, and explicit specification of a sharedconceptualisation [Studer, 1998]

More concretely

An ontology is a shared and formalized vocabulary used to describedata or information to

I enable interoperability

I infer new knowledge

An ontology is a knowledge representation used by agents(computers).

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Page 45: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What is an ontology ?

What do we have in an ontology?

I Labels (lexicon or symbols) : fog, nieba

I Classes (notions, set of objects, event, state,...): Sensor,Location, City, Observation...

I Relations between classes

I Hierarchy of classes: City is a LocationI Properties: Sensor observes parameter, Observation occurs in

Location, ...

I More axioms : A sensor that observes a Temperature is aTemperatureSensor

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Page 46: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

What is an ontology ?

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Page 47: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Description logic

The OWL language for representing ontologies

I a W3C formalism

I relies on Description Logics (DL)

Two types of axioms in DL

I Terminological (T-box) : defining the vocabularyI ssn : Sensor v ssn : DeviceI ssn : Sensor u ssn : Actuator v⊥I ssn : LightSensor ≡ ssn : Sensor u ∃ssn : observes.qudt : Light

I Assertional (A-box) : asserting facts on data with thevocabulary (or data semantic description)

I XXX ∈ ssn : Sensor

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Page 48: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

How is the formalization exploited ?

The reasoner

I is a program which uses the vocabulary, the logic in which it isrepresented and the assertions made on data to infer newfacts.

I can detect inconsistencies when logical rules are violated.

An inference

I corresponds to a new fact deduced by the reasoner

I relies on different characterics of the used logic

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Page 49: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Inference based on properties

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Page 50: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Inference based on properties

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Page 51: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Inference based on the hierarchy of classes

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Page 52: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Inference based on class definition

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Page 53: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Inference based on class definition

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Page 54: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Inference based on class definition

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Page 55: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Different levels of formalization

Type Content LanguageLightweight ontology Class hierarchy and properties RDFSHeavyweight ontology Lightweight ontology - More axioms OWL

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Page 56: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Different ”roles” for ontologies

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Page 57: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Ontology Alignment

Alignment

I Finding corresponding entities between ontologies orknowledge bases

I The possible links :I Equivalence/DisjointednessI Specialization/Generalization/Composition

I A branch of Semantic Web is dedicated to the study ofautomated ways for generating alignments

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Page 58: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Example of Alignment

SSN and SAREF : T-box Alignments

I saref:Device ≡ ssn:Device

I ssn:startTime ⊂ saref:Time

I saref:UnitOfMeasure ⊂ ssn:MeasuringCapability

A-box Alignment

I Toulouse : http://sws.geonames.org/2972315 ≡http://fr.dbpedia.org/resource/Toulouse

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Page 59: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Alignment and reasoning

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Page 60: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Alignment and reasoning

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Page 61: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

Alignment and reasoning

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Page 62: Semantic Web - Introduction to semantic data€¦ · Semantic Web Introduction to semantic data Catherine Comparot Nathalie Hernandez Cassia Trojahn UT2J - IRIT, MELODI Team 7th June,

The semantic Web for GeoScienceA project using ontologies for both interoperability andreasoning

http://citydata.ai.wu.ac.at/

KPIDataPipeline/

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