Introduction to the Semantic Web

Introduction to the
Semantic Web
Oscar Corcho (ocorcho@fi.upm.es)
Universidad Politécnica de Madrid

Universidad del Valle, Cali, Colombia
September 7th 2010

Acknowledgements: Asunción Gómez-Pérez, Jesús Barrasa,
Angel López Cima, Oscar Muñoz, Jose Angel Ramos Gargantilla,
María del Carmen Suárez de Figueroa, Boris Villazón, Mariano
Fernández López, Luis Vilches, Carlos Ruíz Moreno

Work distributed under the license Creative Commons Attribution-
Noncommercial-Share Alike 3.0

Overview

• Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the
Web of Linked Data and all its applications
• The Web (1.0 and 2.0)
• Web applications
• The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0)
• Semantic Web Applications Or [Semantic | Web]+ Applications
• The Web of Linked Data
• Linked Data Applications
• Semantic-based Applications
• preSemanticWeb Applications
• Annotation
• Semantic Web 1.0 Applications
• Annotation, Data Integration and Decision Support Systems
• Semantic Web 3.0 Applications
• (Collaborative) Annotation and Data Integration
• Conclusions and Trends

Health and Safety Notice

Classification
Disclaimer: This is not the only way that applications can be classified or
grouped. In fact, many other possibilities exist for the classification of
Semantic Web application.

The beginning: Web 1.0

WWW
HTTP
URI

From Web1.0 to Web2.0

More than
30M pages

More than
1000M users

New requirements start arising
• Cooperation
WWW
• Dynamicity
HTTP, HTML, URI • Decentralised change
• Heterogeneity
• Multimedia content

Web2.0 basic sites and services

Web1.0 vs Web2.0

• Cooperation
• Dynamicity
• Decentralised change
• Heterogeneity
• Multimedia content

Web Applications

• Who doesn‟t know what is a Web application?
• Let‟s define it
• A web application is an application that is accessed over a
network such as the Internet or an intranet.
• The term may also mean a computer software application that is…
• … hosted in a browser-controlled environment (e.g. a Java
applet)
• … or coded in a browser-supported language (such as
JavaScript, combined with a browser-rendered markup
language like HTML)
• … and reliant on a common web browser to render the
application executable.
• Some comments
• Too many technology-related terms in the definition
• No mentions to the evolution of user-generated content (Web1.0 
Web2.0), although it is already well understood.

(Syntactic) Web Limitations

Resource
href href href

Resource Resource Resource Resource

href href href
href Resource

href href
href
Resource Resource Resource

href href

Resource

• A place where computers do the
presentation (easy) and people do
the linking and interpreting (hard).
• Why not get computers to do more
of the hard work?

Hard Work using the Syntactic Web…

Find images of Oscar Corcho

…Marta Millán (Universidad del Valle)…

What’s the Problem?

• Typical web page
markup consists of:
• Rendering information
(e.g., font size and
colour)
• Hyper-links to related
content
• Semantic content is
accessible to humans
but not (easily) to
computers…

Information we can see…
Universidad del Valle
Organización Universitaria
Alumnos
Investigación
Deporte
Eventos y actividades… (en la universidad/fuera…?)

Noticias

Tipos de personas a los que va dirigido
Alumnos
Profesores
Personal de Administración y Servicios

…

Information a machine can see…

WWW2002
The eleventh inteqnational woqld wide webcon
Sheqaton waikiki hotel
Honolulu, hawaii, USA
7-11 may 2002
1 location 5 days leaqn inteqact
Registeqed paqticipants coming fqom
austqalia, canada, chile denmaqk, fqance,
geqmany, ghana, hong kong, india, iqeland,
italy, japan, malta, new zealand, the
netheqlands, noqway, singapoqe, switzeqland,
the united kingdom, the united states,
vietnam, zaiqe
Registeq now
On the 7th May Honolulu will pqovide the
backdqop of the eleventh inteqnational woqld
wide web confeqence. This pqestigious event 
Speakeqs confiqmed
Tim beqneqs-lee
Tim is the well known inventoq of the Web,…

Solution: XML markup with “meaningful”
tags?

<name>WWW2002
The eleventh inteqnational woqld wide webcon</name>
<date>7-11 may 2002</date>
<location>Sheqaton waikiki hotel
Honolulu, hawaii, USA</location>
<introduction>Registeq now
Speakeqs confiqmed</introduction>
<speaker>Tim beqneqs-lee
<bio>Tim is the well known inventoq of the Web,</bio>
</speaker>
</speaker>
<registration>Registeqed paqticipants coming fqom
netheqlands, noqway, singapoqe, switzeqland, the
united kingdom, the united states, vietnam,
zaiqe<registration>

But What About…?

<conf>WWW2002
The eleventh inteqnational woqld wide webcon</conf>
<place>Sheqaton waikiki hotel
Honolulu, hawaii, USA</place>
<introduction>Registeq now
Speakeqs confiqmed</introduction>
</speaker>
</speaker>
<registration>Registeqed paqticipants coming fqom
zaiqe<registration>

Still the Machine only sees…

<conf>WWW2002
The eleventh inteqnational woqld wide webcon<conf>
<place>Sheqaton waikiki hotel
Honolulu, hawaii, USA<place>
<intqoduction>Registeq now
Speakeqs confiqmed</intqoduction>
<speakeq>Tim beqneqs-lee
<bio>Tim is the well known inventoq of the
Web,</bio>
</speakeq>
<speakeq>Tim beqneqs-lee
<bio>Tim is the well known inventoq of the
Web,</bio>
</speakeq>
<qegistqation>Registeqed paqticipants coming fqom

What is the Semantic Web?
• An extension of the current
Web…
• … where information and services
are given well-defined and
explicitly represented meaning, …
• … so that it can be shared and
used by humans and machines, ...
• ... better enabling them to work in
cooperation

• How?
• Promoting information exchange
by tagging web content with
machine processable descriptions
of its meaning.
• And technologies and
infrastructure to do this

Need to Add “Semantics”

• Agreement on the meaning of annotations
• Shared understanding of a domain of interest
• Formal and machine manipulable model of a domain of interest

• An ontology is an engineering artifact, which provides:
• A vocabulary of terms
• A set of explicit assumptions regarding the intended meaning of the
vocabulary.
• Almost always including concepts and their classification
• Almost always including properties between concepts

• Besides...
• The meaning (semantics) of such terms is formally specified
• New terms can be formed by combining existing ones
• Can also specify relationships between terms in multiple ontologies

Types of ontologies

Thesauri General
“narrower term” Frames Logical
Formal
relation (properties) constraints
Catalog/ID is-a

Terms/ Informal Formal Value Disjointness,
glossary is-a instance Restrs. Inverse, part-Of ...

 Lassila O, McGuiness D (2001) The Role of Frame-Based Representation on the
Semantic Web. Technical Report. Knowledge Systems Laboratory. Stanford University.
KSL-01-02

A catalog

Consoles and Accessories (1150)
Amiga (12)
Amstrad (28)
Atari (22)
Commodore (13)
Microsoft (31)
Xbox (20)
Catalog: finite list of terms. It can provide
Xbox360 (11)
an unambiguous interpretation of terms.
Nintendo (338)
(E.g. 1150 unambiguously denotes
GameBoy (47)
“consoles and accessories“.)
GameBoy Advance (40)
GameBoy Color (16)
Gamecube (38)
GameBoy Micro (2)
Nintendo 64 (74)
http://www.todocoleccion.net/catalogo.cfm
SuperNintendo (51)
Nintendo DS (52)
Nintendo wii (16)

A glossary of terms

Action - Proceeding taken in a court of law. Synonymous
with case, suit lawsuit.

Adjudication - A judgment or decree

Adversary system - Basic U.S. trial system in which each
Glossary: list of terms and meanings,
of the opposing parties has opportunity to state his
which are expressed as natural language
viewpoints before the court. Plaintiff argues for defendant's
statements.
guilt (criminal) or liability (civil). Defense argues for
defendant's innocence (criminal) or against liability civil)

Affidavit - A written or printed declaration or statement http://www.headinjury.com/lawglossary.htm
under oath

Affirm - The assertion of an appellate court that the
judgment of the lower court is correct and should stand.

Thesauri
Agricultural economics MT 6.35 Agriculture
FR Agroéconomie
SP Economía agraria
NT1 Agricultural credit
NT1 Agricultural development Thesaurus: list of terms that specifies
NT2 Subsistence agriculture what terms are preferred, the relation
NT1 Agricultural markets narrower term, etc. (e.g. “agricultural
NT1 Agricultural planning credit“ is a narrower term [NT] than
NT1 Agricultural policy “agricultural economics“)
NT2 Agricultural prices
NT2 Food security
NT1 Agricultural production
NT1 Agricultural statistics
NT2 Food statistics
NT1 Land economics
NT2 Agrarian structure http://www2.ulcc.ac.uk/unesco/
NT3 Land reform
NT2 Farm size
NT2 Land reclamation
A searching system uses ”agroindustry” when the query
NT2 Land tenure
includes ”agrocultural industry”,
NT2 Land value
RT Agricultural enterprises
RT Agroindustry Agricultural industry USE Agroindustry
RT Rural economy

Informal is-a

Term hierarchies: they
provide a general notion of
generalization and specialization.

http://dir.yahoo.com/

Formal is-a

Strict subclass hierarchies: if A is a subclass of B,
then if an object is an instance of A necessarily
implies that the object is instance of B.

http://www.xml.com/pub/a/2005/01/26/formtax.html

…
<owl:Class rdf:ID="Transportation"/>
<owl:Class rdf:ID="AirVehicle">
<rdfs:subClassOf rdf:resource="#Transportation"/>
</owl:Class>
<owl:Class rdf:about="#GroundVehicle">
<rdfs:subClassOf rdf:resource="#Transportation"/>
</owl:Class>
<owl:Class rdf:about="#Automobile">
<rdfs:subClassOf> <owl:Class rdf:ID="GroundVehicle"/>
</rdfs:subClassOf>
</owl:Class>
<owl:Class rdf:ID="Truck">
<rdfs:subClassOf> <owl:Class rdf:about="#GroundVehicle"/>
</rdfs:subClassOf>
</owl:Class>
…

Logical constraints

Ontologies with general logical constraints:
they include constraints, explicit formal definitions,
etc.


<owl:ObjectProperty rdf:about="#hasDeparturePlace">
<rdfs:domain rdf:resource="#travel"/>
<rdfs:range rdf:resource="#place"/>
</owl:ObjectProperty>


<owl:ObjectProperty rdf:about="#hasArrivalPlace">
<rdfs:domain rdf:resource="#travel"/>
<rdfs:range rdf:resource="#place"/>
</owl:ObjectProperty>
TRAVEL

 Gómez-Pérez A, Fernández-López M, Corcho O (2003) Has departure place Has arrival place
Ontological engineering. Springer-Verlag, London

PLACE

travel (= 1 departurePlace.place) (= 1 arrivalPlace.place) ( hasTransportMean.string)

Ontology Languages
• A large amount of work on Semantic Web has concentrated on
the definition of a collection or “stack” of languages.
• Used to support the representation and use of metadata
• Basic machinery that we can use to represent the extra semantic
information needed for the Semantic Web

SWRL

OWL

RDFS
RDF(S)
RDF

XML

Index

• Resource Description Framework (RDF)
• RDF primitives
• Reasoning with RDF
• RDF Schema
• RDF Schema primitives
• Reasoning with RDFS
• RDF(S) Management APIs
• SPARQL
• OWL

29

RDF: Resource Description Framework

• W3C recommendation
• RDF is graphical formalism ( + XML syntax + semantics)
• For representing metadata
• For describing the semantics of information in a machine-
accessible way
• Resources are described in terms of properties and property
values using RDF statements
• Statements are represented as triples, consisting of a subject,
predicate and object. [S, P, O]

“Oscar Corcho García”
person:hasName

person:hasColleague
oeg:Oscar oeg:Asun

person:hasColleague person:hasHomePage

oeg:Raul “http://www.fi.upm.es/”

30

URIs (Universal-Uniform Resource Identifer)

• Two types of identifiers can be used to identify Linked
Data resources
• Hash URIs or URIRefs (Unique Resource Identifiers
References)
• A URI and an optional Fragment Identifier separated
from the URI by the hash symbol „#‟
• http://www.ontology.org/people#Person
• people:Person
• Slash URIs or plain URIs can also be used, as in FOAF:
• http://xmlns.com/foaf/0.1/Person

31

RDF Serialisations

• Normative
• RDF/XML (www.w3.org/TR/rdf-syntax-grammar/)
• Alternative (for human consumption)
• N3 (http://www.w3.org/DesignIssues/Notation3.html)
• Turtle (http://www.dajobe.org/2004/01/turtle/)
• TriX (http://www.w3.org/2004/03/trix/)
• …

Important: the RDF serializations allow different
syntactic variants. E.g., the order of RDF statements
has no meaning

32

RDF Serialisations. RDF/XML

<?xml version="1.0"?>
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:person="http://www.ontologies.org/ontologies/people#"
xmlns="http://www.oeg-upm.net/ontologies/people#"
xml:base="http://www.oeg-upm.net/ontologies/people">

<rdf:Property rdf:about="http://www.ontologies.org/ontologies/people#hasHomePage"/>
<rdf:Property rdf:about="http://www.ontologies.org/ontologies/people#hasColleague"/>
<rdf:Property rdf:about="http://www.ontologies.org/ontologies/people#hasName"/>

<rdf:Description rdf:about="#Raul"/>
<rdf:Description rdf:about="#Asun">
<person:hasColleague rdf:resource="#Raul"/>
<person:hasHomePage>http://www.fi.upm.es</person:hasHomePage>
</rdf:Description>
<rdf:Description rdf:about="#Oscar">
<person:hasColleague rdf:resource="#Asun"/>
<person:hasName>Oscar Corcho García</person:hasName>
</rdf:Description>

</rdf:RDF>

33

RDF Serialisations. N3

@base <http://www.oeg-upm.net/ontologies/people >
@prefix person: <http://www.ontologies.org/ontologies/people#>
:Asun person:hasColleague :Raul ;
person:hasHomePage “http://www.fi.upm.es/”.
:Oscar person:hasColleague :Asun ;
person:hasName “Óscar Corcho García”.

34

Exercise

• Objective
• Get used to the different syntaxes of RDF
• Tasks
• Take the text of an RDF file and create its corresponding graph
• Take an RDF graph and create its corresponding RDF/XML and N3 files

35

Exercise 1.a. Create a graph from a file

• Open the file StickyNote_PureRDF.rdf

• Create the corresponding graph from it

• Compare your graph with those of your colleagues

36

Exercise 1.a. StickyNote_PureRDF.rdf

37

Exercise 1.b. Create files from a graph

• Transform the following graph into N3 syntax

hasMeasurement
Measurement8401
includes Sensor029

hasTemperature atTime
Class01

includes
29 2010-06-12T12:00:12

Computer101
hasOwner
User10A

hasName

Pedro

38

Blank nodes: structured property values

• Most real-world data involves structures that are more
complicated than sets of RDF triple statements

This intermediate URI does not
“Oscar Corcho García” need to have a name
person:hasName

person:hasPostalAddress
oeg:Oscar

address:city address:hasStreetName

city:BoadillaDelMonte Campus de Montegancedo s/n

• In RDF/XML, it is an <rdf:Description> node with no
rdf:about
• In N3, it is a resource identifier that starts with „_‟
• E.g., “_:nodeX”

39

Typed literals

• So far, all values have been presented as strings
• XML Schema datatypes can be used to specify
values (objects in some RDF triple statements)

person:hasBirthDate
oeg:Oscar 1976-02-02

• In RDF/XML, this is expressed as:
• <rdf:Description rdf:about=”#Oscar”>
<person:hasBirthDate
rdf:datatype="http://www.w3.org/2001/XMLSchema#date">1976-02-02
</person:hasBirthDate>
</rdf:Description>

• In N3, this is expressed as:
• oeg:Oscar person:hasBirthDate ”1976-02-02”^^xsd:date .

40

RDF Containers

• There is often the need to describe groups of things
• A book was created by several authors
• A lesson is taught by several persons
• etc.
• RDF provides a container vocabulary
• rdf:Bag  A group of resources or literals, possibly including
duplicate members, where the order of members is not
significant
• rdf:Seq  A group of resources or literals, possibly including
duplicate members, where the order of members is significant
• rdf:Alt  A group of resources or literals that are alternatives
(typically for a single value of a property)

person:hasEmailAddress rdf:type
oeg:Oscar rdf:Seq

rdf:_1 rdf:_2

“ocorcho@fi.upm.es” “oscar.corcho@upm.es”

41

RDF Reification

• RDF statements about other RDF statements
• “Raúl believes that Oscar‟s birthdate is on Feb 2nd, 1976 and that
his e-mail address is ocorcho@fi.upm.es”

modal:believes
oeg:Raúl oeg:Oscar

person:hasEmailAddress person:hasBirthDate

“ocorcho@fi.upm.es” 02/02/1976

• RDF Reification
• Allows expressing beliefs (and other modalities)
• Allows expressing trust models, digital signatures, etc.
• Allows expressing metadata about metadata

42

Main value of a structured value

• Sometimes one of the values of a structured value is
the main one
• The weight of an item is 2.4 kilograms
• The most important value is 2.4, which is expressed with
rdf:value

• Scarcely used

product:hasWeight
product:Item1
rdf:value units:hasWeightUnit

2.4 units:kilogram

43

Index

• RDF primitives
• RDF Schema
• SPARQL
• OWL

44

RDF inference. Graph matching techniques

• RDF inference is based on graph matching
techniques
• Basically, the RDF inference process consists of the
following steps:
• Transform an RDF query into a template graph that has to
be matched against the RDF graph
• It contains constant and variable nodes, and constant
and variable edges between nodes
• Match against the RDF graph, taking into account constant
nodes and edges
• Provide a solution for variable nodes and edges

45

RDF inference. Examples (I)

• Sample RDF graph
“Oscar Corcho García”
person:hasName

person:hasColleague
oeg:Oscar oeg:Asun

person:hasColleague person:hasHomePage

oeg:Raúl “http://www.fi.upm.es/”

• Query: “Tell me who are the persons who have Asun as a
colleague”
person:hasColleague
? oeg:Asun

• Result: oeg:Oscar and oeg:Raúl

46

RDF inference. Examples (II)

• Query: “Tell me which are the relationships between
Oscar and Asun”
?
oeg:Oscar oeg:Asun

• Result: oeg:hasColleague

• Query: “Tell me the homepage of Oscar colleagues”
person:hasColleague
oeg:Oscar

person:hasHomePage

?

• Result: “http://www.fi.upm.es/”

47

RDF inference. Entailment rules

48

Index

• RDF primitives
• RDF Schema
• SPARQL
• OWL

49

RDFS: RDF Schema

• W3C Recommendation
• RDF Schema extends RDF to enable talking about classes of
resources, and the properties to be used with them
• Class definition: rdfs:Class, rdfs:subClassOf
• Property definition: rdfs:subPropertyOf, rdfs:range, rdfs:domain
• Other primitives: rdfs:comment, rdfs:label, rdfs:seeAlso,
rdfs:isDefinedBy
• RDFS vocabulary adds constraints on models, e.g.:
• x,y,z type(x,y) and subClassOf(y,z) type(x,z)

ex:Animal

rdfs:subClassOf

rdf:type
ex:Oscar ex:Person

50

RDF(S) Serialisations. RDF/XML syntax

<?xml version="1.0"?>
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:person="http://www.ontologies.org/ontologies/people#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns="http://www.oeg-upm.net/ontologies/people#"
xml:base="http://www.oeg-upm.net/ontologies/people">

<rdfs:Class rdf:about="http://www.ontologies.org/ontologies/people#Professor">
<rdfs:subClassOf>
<rdfs:Class rdf:about="http://www.ontologies.org/ontologies/people#Person"/>
</rdfs:subClassOf>
</rdfs:Class>
<rdfs:Class rdf:about="http://www.ontologies.org/ontologies/people#Lecturer">
<rdfs:subClassOf rdf:resource="http://www.ontologies.org/ontologies/people#Person"/>
</rdfs:Class>
<rdfs:Class rdf:about="http://www.ontologies.org/ontologies/people#PhD">
<rdfs:subClassOf rdf:resource="http://www.ontologies.org/ontologies/people#Person"/>
</rdfs:Class>
…

51

RDF(S) Serialisations. RDF/XML syntax

…
<rdf:Property rdf:about="http://www.ontologies.org/ontologies/people#hasHomePage"/>
<rdf:Property rdf:about="http://www.ontologies.org/ontologies/people#hasColleague">
<rdfs:domain rdf:resource=" http://www.ontologies.org/ontologies/people#Person"/>
<rdfs:range rdf:resource=" http://www.ontologies.org/ontologies/people#Person"/>
</rdf:Property>
<rdf:Property rdf:about="http://www.ontologies.org/ontologies/people#hasName">
<rdfs:domain rdf:resource="http://www.w3.org/2002/07/owl#Thing"/>
</rdf:Property>

<person:PhD rdf:ID="Raul"/>
<person:Professor rdf:ID=“Asun">
<person:hasColleague rdf:resource="#Raul"/>
<person:hasHomePage>http://www.fi.upm.es</person:hasHomePage>
</person:Professor>
<person:Lecturer rdf:ID="Oscar">
<person:hasColleague rdf:resource="#Asun"/>
<person:hasName>Óscar Corcho García</person:hasName>
</person:Lecturer>
</rdf:RDF>

52

RDF(S) Serialisations. N3

@base <http://www.oeg-upm.net/ontologies/people >
@prefix person: <http://www.ontologies.org/ontologies/people#>
person:hasColleague a rdf:Property;
rdfs:domain person:Person;
rdfs:range person:Person.
person:Professor rdfs:subClassOf person:Person.
person:Lecturer rdfs:subClassOf person:Person.
person:PhD rdfs:subClassOf person:Person.
:Asun a person:Professor;
person:hasColleague :Raul ;
person:hasHomePage “http://www.fi.upm.es/”.
:Oscar a person:Lecturer;
person:hasColleague :Asun ;
person:hasName “Óscar Corcho García”.
:Raul a person:PhD.
a is equivalent to rdf:type

53

RDF(S) Example
RDFS
rdfs:Literal rdfs:Class

rdf:Type
rdfs:range

rdfs:domain
rdf:Type Flight arrivalDate
rdfs:domain rdfs:domain
rdfs:domain departureDate

company-name singleFare rdfs:range
rdfs:range
rdfs:range rdf:Type

units:currencyQuantity rdf:Type
rdf:Type rdf:Type time:Date

RDF

rdf:Property
rdf:Type
rdf:Type
company-name
rdf:Type

“Iberia” arrivalDate
IB-4321
singleFare departureDate
10/11/2005
500 euros
54

Exercise

•Objective
• Get used to the different syntaxes of RDF(S)
•Tasks
• Take the text of an RDF(S) file and create its corresponding graph
• Take an RDF(S) graph and create its corresponding RDF/XML and N3 files

55

Exercise 2.a. Create a graph from a file

• Open the files StickyNote.rdf and StickyNote.rdfs

• Create the corresponding graph from them

• Compare your graph with those of your colleagues

56

Exercise 2.a. StickyNote.rdf

57

Exercise 2.a. StickyNote.rdfs

58

Exercise 2.b. Create files from a graph

• Transform the following graph into N3 syntax

Room Object Measurement Person

hasMeasurement
includes Sensor029

hasTemperature atTime
Class01

includes
29 2010-06-12T12:00:12

Computer101
hasOwner
User10A

hasName

Pedro

59

Index

• RDF primitives
• RDF Schema
• SPARQL
• OWL

60

RDF(S) inference. Entailment rules

61

RDF(S) inference. Additional inferences

62

RDF(S) limitations

• RDFS too weak to describe resources in sufficient detail
• No localised range and domain constraints
• Can‟t say that the range of hasChild is person when applied to
persons and elephant when applied to elephants
• No existence/cardinality constraints
• Can‟t say that all instances of person have a mother that is also a
person, or that persons have exactly 2 parents
• No boolean operators
• Can‟t say or, not, etc.
• No transitive, inverse or symmetrical properties
• Can‟t say that isPartOf is a transitive property, that hasPart is the
inverse of isPartOf or that touches is symmetrical
• Difficult to provide reasoning support
• No “native” reasoners for non-standard semantics
• May be possible to reason via FOL axiomatisation

63

Exercise

•Objective
• Understand the features of RDF(S) for implementing ontologies, including its
limitations
•Tasks
• Given a scenario description, build a simple ontology in RDF Schema

64

Exercise 3. Domain description

• Un lugar puede ser un lugar de interés.
• Los lugares de interés pueden ser lugares turísticos o
establecimientos, pero no las dos cosas a la vez.
• Los lugares turísticos pueden ser palacios, iglesias, ermitas y
catedrales.
• Los establecimientos pueden ser hoteles, hostales o albergues.
• Un lugar está situado en una localidad, la cual a su vez puede ser
una villa, un pueblo o una ciudad.
• Un lugar de interés tiene una dirección postal que incluye su calle y
su número.
• Las localidades tienen un número de habitantes.
• Las localidades se encuentran situadas en provincias.

• Covarrubias es un pueblo con 634 habitantes de la provincia de
Burgos.
• El restaurante “El Galo” está situado en Covarrubias, en la calle
Mayor, número 5.
• Una de las iglesias de Covarrubias está en la calle de Santo
Tomás.

65

Exercise 3. Sample resulting ontology

66

Index

• RDF primitives
• RDF Schema
• SPARQL
• OWL

67

Sample RDF APIs

• RDF libraries for different languages:
• Java, Python, C, C++, C#, .Net, Javascript, Tcl/Tk, PHP, Lisp, Obj-C,
Prolog, Perl, Ruby, Haskell
• List in http://esw.w3.org/topic/SemanticWebTools

• Usually related to a RDF repository

• Multilanguage:
• Redland RDF Application Framework (C, Perl, PHP, Python and Ruby):
http://www.redland.opensource.ac.uk/
• Java:
• Jena: http://jena.sourceforge.net/
• Sesame: http://www.openrdf.org/
• PHP:
• RAP - RDF API for PHP: http://www4.wiwiss.fu-berlin.de/bizer/rdfapi/
• Python:
• RDFLib: http://rdflib.net/
• Pyrple: http://infomesh.net/pyrple/

68

Jena

• Java framework for building Semantic Web
applications
• Open source software from HP Labs
• The Jena framework includes:
• A RDF API
• An OWL API
• Reading and writing RDF in RDF/XML, N3 and N-Triples
• In-memory and persistent storage
• A rule based inference engine
• SPARQL query engine

69

Sesame

• A framework for storage, querying and inferencing of RDF
and RDF Schema
• A Java Library for handling RDF
• A Database Server for (remote) access to repositories of
RDF data
• Highly expressive query and transformation languages
• SeRQL, SPARQL
• Various backends
• Native Store
• RDBMS (MySQL, Oracle 10, DB2, PostgreSQL)
• main memory
• Reasoning support
• RDF Schema reasoner
• OWL DLP (OWLIM)
• domain reasoning (custom rule engine)

70

Jena example. Graph creation

http://.../JohnSmith
vcard:FN vcard:N

John Smith
vcard:Given vcard:Family

John Smith

// some definitions
String personURI = "http://somewhere/JohnSmith";
String givenName = "John";
String familyName = "Smith";
String fullName = givenName + " " + familyName;
// create an empty
Model Model model = ModelFactory.createDefaultModel();
// create the resource
// and add the properties cascading style
Resource johnSmith = model.createResource(personURI)
.addProperty(VCARD.FN, fullName)
.addProperty(VCARD.N, model.createResource()
.addProperty(VCARD.Given, givenName)
.addProperty(VCARD.Family, familyName));

71

Jena example. Read and write
// create an empty model
Model model = ModelFactory.createDefaultModel();

// use the FileManager to find the input file
InputStream in = FileManager.get().open( inputFileName );
if (in == null) {
throw new IllegalArgumentException("File not found");
}
<rdf:RDF
// read the RDF/XML file
model.read(in, ""); xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#'
xmlns:vcard='http://www.w3.org/2001/vcard-rdf/3.0#'
// write it to standard out >
model.write(System.out);
<rdf:Description rdf:nodeID="A0">
<vcard:Family>Smith</vcard:Family>
<vcard:Given>John</vcard:Given>
</rdf:Description>
<rdf:Description rdf:about='http://somewhere/JohnSmith/'>
<vcard:FN>John Smith</vcard:FN>
<vcard:N rdf:nodeID="A0"/>
</rdf:Description>
...
</rdf:RDF>

72

Some RDF editors

• IsaViz
• http://www.w3.org/2001/11/IsaViz/
• Morla
• http://www.morlardf.net/
• RDFAuthor
• http://rdfweb.org/people/damian/RDFAuthor/
• RdfGravity
• http://semweb.salzburgresearch.at/apps/rdf-gravity/
• Rhodonite
• http://rhodonite.angelite.nl/

73

Main References

• Brickley D, Guha RV (2004) RDF Vocabulary
Description Language 1.0: RDF Schema. W3C
Recommendation
http://www.w3.org/TR/PR-rdf-schema/
• Lassila O, Swick R (1999) Resource Description
Framework (RDF) Model and Syntax Specification.
W3C Recommendation
http://www.w3.org/TR/REC-rdf-syntax/
• RDF validator:
http://www.w3.org/RDF/Validator/
• RDF resources:
http://planetrdf.com/guide/

74

Index

• RDF primitives
• RDF Schema
• SPARQL
• OWL

75

RDF(S) query languages

• Languages developed to allow accessing datasets expressed in RDF(S)
(and in some cases OWL)
Application Application

SQL queries SPARQL, RQL, etc., queries

Relational RDF(S)
DB OWL

• Supported by the most important language APIs
• Jena (HP labs)
• Sesame (Aduna)
• Boca (IBM)
• ...
• There are some differences wrt. languages like SQL, such as
• Combination of different sources
• Trust management
• Open World Assumption

76

Query types

• Selection and extraction
• “Select all the essays, together with their authors and their authors‟
names”
• “Select everything that is related to the book „Bellum Civille‟”
• Reduction: we specify what it should not be returned
• “Select everything except for the ontological information and the book
translators”
• Restructuring: the original structure is changed in the final result
• “Invert the relationship „author‟ by „is author of‟”
• Aggregation
• “Return all the essays together with the mean number of authors per
essay”
• Combination and inferences
• “Combine the information of a book called „La guerra civil‟ and whose
author is Julius Caesar with the book whose identifier is „Bellum Civille‟”
• “Select all the essays, together with its authors and author names”,
including also the instances of the subclasses of Essay
• “Obtain the relationship „coauthor‟ among persons who have written the
same book”

77

RDF(S) query language families

• SquishQL Family Triple • RQL Family Description
• SquishQL
database • RQL graphs
Query Query
• SeRQL
• rdfDB Query Language
structure semantics
• eRQL
• RDQL • Controlled natural language
• BRQL • Metalog
• TriQL • Other
• XPath, XSLT, XQuery XML • Algae
• XQuery for RDF repository • iTQL
Query • N3QL
• XsRQL syntax
• PerlRDF Query Language
• TreeHugger and RDFTwig • RDEVICE Deductive
• RDFT, Nexus Query Language
Language • RDFQBE
• RDFPath, Rpath and RXPath • RDFQL
• Versa • TRIPLE
SPARQL • WQL
W3C Recommendation
15 January 2008

78

SPARQL

• SPARQL Protocol and RDF Query Language
• Supported by: Jena, Sesame, IBM Boca, etc.
• Features
• It supports most of the aforementioned queries
• It supports datatype reasoning (datatypes can be requested instead
of actual values)
• The domain vocabulary and the knowledge representation
vocabulary are treated differently by the query interpreters
• It allows making queries over properties with multiple values, over
multiple properties of a resource and over reifications
• Queries can contain optional statements
• Some implementations support aggregation queries
• Limitations
• Neither set operations nor existential or universal quantifiers can be
included in the queries
• It does not support recursive queries

79

SPARQL is also a protocol

• SPARQL is 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=PREFIXfoaf:
<http://xmlns.com/foaf/0.1/...

• Services running SPARQL queries over a set of graphs
• A transport protocol for invoking the service
• Based on ideas from earlier protocol work such as Joseki
• Describing the service with Web Service technologies

80

SPARQL Endpoints
• SPARQL protocol services
• Enables users (human or other) to query a knowledge base using
SPARQL
• Results are typically returned in one or more machine-processable
formats
• List of SPARQL Endpoints
• http://esw.w3.org/topic/SparqlEndpoints
• Programmatic access using libraries:
• ARC, RAP, Jena, Sesame, Javascript SPARQL, PySPARQL, etc.
• Examples:

Project Endpoint
DBpedia http://dbpedia.org/sparql
BBC Programmes and Music http://bbc.openlinksw.com/sparql/
data.gov http://semantic.data.gov/sparql
data.gov.uk http://data.gov.uk/sparql
Musicbrainz http://dbtune.org/musicbrainz/sparql

81

A simple SPARQL query
Data:

@prefix dc: <http://purl.org/dc/elements/1.1/> .
@prefix : <http://example.org/book/> .
:book1 dc:title "SPARQL Tutorial" .

Query:
SELECT ?title
WHERE
{
<http://example.org/book/book1> <http://purl.org/dc/elements/1.1/title> ?title .
}
Query result: title
"SPARQL Tutorial"

• A pattern is matched against the RDF data
• Each way a pattern can be matched yields a solution
• The sequence of solutions is filtered by: Project, distinct, order, limit/offset
• One of the result forms is applied: SELECT, CONSTRUCT, DESCRIBE, ASK

82

Graph patterns

• Basic Graph Patterns, where a set of triple patterns
must match
• Group Graph Pattern, where a set of graph patterns
must all match
• Optional Graph patterns, where additional patterns
may extend the solution
• Alternative Graph Pattern, where two or more
possible patterns are tried
• Patterns on Named Graphs, where patterns are
matched against named graphs

83

Multiple matches

@prefix foaf: <http://xmlns.com/foaf/0.1/> .

_:a foaf:name "Johnny Lee Outlaw" .
_:a foaf:mbox <mailto:jlow@example.com> .
_:b foaf:name "Peter Goodguy" .
_:b foaf:mbox <mailto:peter@example.org> .
_:c foaf:mbox <mailto:carol@example.org> .

SELECT ?name ?mbox
WHERE
{ ?x foaf:name ?name .
?x foaf:mbox ?mbox }

name mbox
"Johnny Lee Outlaw" <mailto:jlow@example.com>
"Peter Goodguy" <mailto:peter@example.org>

84

Matching RDF literals

@prefix dt: <http://example.org/datatype#> .
@prefix ns: <http://example.org/ns#> .
@prefix : <http://example.org/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

:x ns:p "cat"@en .
:y ns:p "42"^^xsd:integer .
:z ns:p "abc"^^dt:specialDatatype .

SELECT ?v WHERE { ?v ?p "cat" } v

v
SELECT ?v WHERE { ?v ?p "cat"@en }
<http://example.org/ns#x>

v
SELECT ?v WHERE { ?v ?p 42 }
<http://example.org/ns#y>

SELECT ?v WHERE { ?v ?p "abc"^^<http://example.org/datatype#specialDatatype> }

v
<http://example.org/ns#z>

85

Blank node labels in query results


_:a foaf:name "Alice" .
_:b foaf:name "Bob" .

SELECT ?x ?name
WHERE { ?x foaf:name ?name }

x name x name
_:c "Alice" = _:r "Alice"
_:d "Bob" _:s "Bob"

86

Group graph pattern

SELECT ?name ?mbox
WHERE { { ?x foaf:name ?name . }
{ ?x foaf:mbox ?mbox . }
}

SELECT ?x
WHERE {}

SELECT ?name ?mbox
WHERE { { ?x foaf:name ?name . }
{ ?x foaf:mbox ?mbox . FILTER regex(?name, "Smith")}
}

87

Optional graph patterns

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

_:a rdf:type foaf:Person .
_:a foaf:mbox <mailto:alice@example.com> .
_:a foaf:mbox <mailto:alice@work.example> .

_:b rdf:type foaf:Person .

SELECT ?name ?mbox
WHERE { ?x foaf:name ?name .
OPTIONAL { ?x foaf:mbox ?mbox }
}

name mbox
"Alice" <mailto:alice@example.com>
"Alice" <mailto:alice@work.example>
“Bob"

88

Multiple optional graph patterns


_:a foaf:homepage <http://work.example.org/alice/> .

_:b foaf:mbox <mailto:bob@work.example> .

SELECT ?name ?mbox ?hpage
WHERE { ?x foaf:name ?name .
OPTIONAL { ?x foaf:mbox ?mbox } .
OPTIONAL { ?x foaf:homepage ?hpage }
}

name mbox hpage
"Alice" <http://work.example.org/alice/>
“Bob" <mailto:bob@work.example>

89

Alternative graph patterns

@prefix dc10: <http://purl.org/dc/elements/1.0/> .
@prefix dc11: <http://purl.org/dc/elements/1.1/> .

_:a dc10:title "SPARQL Query Language Tutorial" .
_:a dc10:creator "Alice" .
_:b dc11:title "SPARQL Protocol Tutorial" .
_:b dc11:creator "Bob" .
_:c dc10:title "SPARQL" .
_:c dc11:title "SPARQL (updated)" .

PREFIX dc10: <http://purl.org/dc/elements/1.0/> title
PREFIX dc11: <http://purl.org/dc/elements/1.1/> "SPARQL Protocol Tutorial"
SELECT ?title
"SPARQL"
WHERE { { ?book dc10:title ?title } UNION
{ ?book dc11:title ?title } } "SPARQL (updated)"
"SPARQL Query Language Tutorial"

SELECT ?x ?y x y
WHERE { { ?book dc10:title ?x } UNION
"SPARQL (updated)"
{ ?book dc11:title ?y } }
"SPARQL Protocol Tutorial"
"SPARQL"
"SPARQL Query Language Tutorial"
SELECT ?title ?author
WHERE author title
{ { ?book dc10:title ?title . ?book dc10:creator ?author }
"Alice" "SPARQL Protocol Tutorial"
UNION
{ ?book dc11:title ?title . ?book dc11:creator ?author }} “Bob” "SPARQL Query Language Tutorial"

90

Patterns on named graphs

# Named graph: http://example.org/foaf/aliceFoaf
@prefix foaf:<http://.../foaf/0.1/> .
@prefix rdf:<http://.../1999/02/22-rdf-syntax-ns#> .
@prefix rdfs:<http://.../2000/01/rdf-schema#> .

_:a foaf:mbox <mailto:alice@work.example> .
_:a foaf:knows _:b .

_:b foaf:nick "Bobby" .
_:b rdfs:seeAlso <http://example.org/foaf/bobFoaf> .

<http://example.org/foaf/bobFoaf>
rdf:type foaf:PersonalProfileDocument .

# Named graph: http://example.org/foaf/bobFoaf
@prefix foaf:<http://.../foaf/0.1/> .
@prefix rdf:<http://.../1999/02/22-rdf-syntax-ns#> .
@prefix rdfs:<http://.../2000/01/rdf-schema#> .

_:z foaf:mbox <mailto:bob@work.example> .
_:z rdfs:seeAlso <http://example.org/foaf/bobFoaf> .
_:z foaf:nick "Robert" .

<http://example.org/foaf/bobFoaf>
rdf:type foaf:PersonalProfileDocument .

91

Patterns on named graphs II


SELECT ?src ?bobNick
FROM NAMED <http://example.org/foaf/aliceFoaf> src bobNick
FROM NAMED <http://example.org/foaf/bobFoaf> <http://example.org/foaf/aliceFoaf> "Bobby"
WHERE
{ <http://example.org/foaf/bobFoaf> "Robert"
GRAPH ?src
{ ?x foaf:mbox <mailto:bob@work.example> .
?x foaf:nick ?bobNick
}
}

PREFIX data: <http://example.org/foaf/>

SELECT ?nick
FROM NAMED <http://example.org/foaf/aliceFoaf>
nick
FROM NAMED <http://example.org/foaf/bobFoaf>
WHERE "Robert"
{
GRAPH data:bobFoaf {
?x foaf:mbox <mailto:bob@work.example> .
?x foaf:nick ?nick }
}

92

Restricting values

@prefix dc: <http://purl.org/dc/elements/1.1/> .
@prefix : <http://example.org/book/> .
@prefix ns: <http://example.org/ns#> .

:book1 dc:title "SPARQL Tutorial" .
:book1 ns:price 42 .
:book2 dc:title "The Semantic Web" .
:book2 ns:price 23 .

PREFIX dc: <http://purl.org/dc/elements/1.1/>
SELECT ?title title
WHERE { ?x dc:title ?title
FILTER regex(?title, "^SPARQL") "SPARQL Tutorial"
}

SELECT ?title title
WHERE { ?x dc:title ?title
FILTER regex(?title, "web", "i" ) "The Semantic Web"
}

PREFIX ns: <http://example.org/ns#> title price
SELECT ?title ?price
WHERE { ?x ns:price ?price . "The Semantic Web" 23
FILTER (?price < 30.5)
?x dc:title ?title . }

93

Value tests

• Based on XQuery 1.0 and XPath 2.0 Function and
Operators
• XSD boolean, string, integer, decimal, float, double,
dateTime
• Notation <, >, =, <=, >= and != for value comparison
Apply to any type
• BOUND, isURI, isBLANK, isLITERAL
• REGEX, LANG, DATATYPE, STR (lexical form)
• Function call for casting and extensions functions

94

Solution sequences and modifiers

• Order modifier: put the solutions in SELECT ?name
order WHERE { ?x foaf:name ?name ; :empId ?emp }
ORDER BY ?name DESC(?emp)
• Projection modifier: choose certain SELECT ?name
variables WHERE
{ ?x foaf:name ?name }
• Distinct modifier: ensure solutions
in the sequence are unique SELECT DISTINCT ?name
• Reduced modifier: permit
elimination of some non-unique SELECT REDUCED ?name
solutions WHERE { ?x foaf:name ?name }

• Limit modifier: restrict the number of
solutions SELECT ?name
LIMIT 20
• Offset modifier: control where the
solutions start from in the overall SELECT ?name WHERE { ?x foaf:name ?name }
sequence of solutions ORDER BY ?name
LIMIT 5
OFFSET 10

95

SPARQL query forms

• SELECT
• Returns all, or a subset of, the variables bound in a query
pattern match
• CONSTRUCT
• Returns an RDF graph constructed by substituting variables
in a set of triple templates
• ASK
• Returns a boolean indicating whether a query pattern
matches or not
• DESCRIBE
• Returns an RDF graph that describes the resources found

96

SPARQL query forms: SELECT


_:a foaf:knows _:b .
_:a foaf:knows _:c .


_:c foaf:name "Clare" .
_:c foaf:nick "CT" .

SELECT ?nameX ?nameY ?nickY
WHERE
{ ?x foaf:knows ?y ;
foaf:name ?nameX .
?y foaf:name ?nameY .
OPTIONAL { ?y foaf:nick ?nickY }
}

nameX nameY nickY
"Alice" "Bob"
"Alice" "Clare" "CT"

97

SPARQL query forms: CONSTRUCT


_:a foaf:mbox <mailto:alice@example.org> .

PREFIX vcard: <http://www.w3.org/2001/vcard-rdf/3.0#>

CONSTRUCT { <http://example.org/person#Alice> vcard:FN ?name }


Query result:

@prefix vcard: <http://www.w3.org/2001/vcard-rdf/3.0#> .

<http://example.org/person#Alice> vcard:FN "Alice" .

98

SPARQL query forms: ASK


_:a foaf:homepage <http://work.example.org/alice/> .


ASK { ?x foaf:name "Alice" }

Query result:

yes

99

SPARQL query forms: DESCRIBE

PREFIX ent: <http://org.example.com/employees#>
DESCRIBE ?x WHERE { ?x ent:employeeId "1234" }

Query result:

@prefix vcard: <http://www.w3.org/2001/vcard-rdf/3.0> .
@prefix exOrg: <http://org.example.com/employees#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix owl: <http://www.w3.org/2002/07/owl#>

_:a exOrg:employeeId "1234" ;

foaf:mbox_sha1sum "ABCD1234" ;
vcard:N
[ vcard:Family "Smith" ;
vcard:Given "John" ] .

foaf:mbox_sha1sum rdf:type owl:InverseFunctionalProperty .

100

Main References

• Prud‟hommeaux E, Seaborne A (2008) SPARQL
Query Language for RDF. W3C Recommendation
http://www.w3.org/TR/rdf-sparql-query/
• SPARQL validator:
http://www.sparql.org/validator.html
• SPARQL implementations:
http://esw.w3.org/topic/SparqlImplementations
• SPARQL Endpoints
http://esw.w3.org/topic/SparqlEndpoints
• SPARQL in Dbpedia
http://dbpedia.org/sparql
101

Index

• RDF primitives
• RDF Schema
• SPARQL
• OWL

102

OWL Basics (on top of RDF and RDFS)

• Set of constructors for concept expressions
• Booleans: and/or/not
• A Session is a TheoreticalSession or a HandsonSession
• Slides are not the same as Code
• Quantification: some/all
• Sessions must have some EducationalMaterial
• Sessions can only have Presenters that have developed Grid
applications or Grid middleware

• Axioms for expressing constraints
• Necessary and Sufficient conditions on classes
• A Session that hasEducationalMaterial Code is a
HandsonSession.
• Disjointness
• TheoreticalSessions are disjoint with HandsonSessions
• Property characteristics: transitivity, inverse

OWL Ontology Example. BioPAX ontology

• http://www.biopax.org/release/biopax-level2.owl

Description Logics

• A family of logic based Knowledge Representation formalisms
• Descendants of semantic networks and KL-ONE
• Describe domain in terms of concepts (classes), roles (relationships)
and individuals
• Specific languages characterised by the constructors and axioms
used to assert knowledge about classes, roles and individuals.
• Example: ALC (the least expressive language in DL that is
propositionally closed)
• Constructors: boolean (and, or, not)
• Role restrictions

• Distinguished by:
• Model theoretic semantics
• Decidable fragments of FOL
• Closely related to Propositional Modal & Dynamic Logics
• Provision of inference services
• Sound and complete decision procedures for key problems
• Implemented systems (highly optimised)

Structure of DL Ontologies

• A DL ontology can be divided into two parts:
• Tbox (Terminological KB): a set of axioms that describe the
structure of a domain :
• Doctor Person
• Person Man Woman
• HappyFather Man hasDescendant.(Doctor
hasDescendant.Doctor)
• Abox (Assertional KB): a set of axioms that describe a
specific situation :
• John HappyFather
• hasDescendant (John, Mary)

Most common constructors in class definitions

• Intersection: C1 ... Cn Human Male
• Union: C1 ... Cn Doctor Lawyer
• Negation: C Male
• Nominals: {x1} ... {xn} {john} ... {mary}
• Universal restriction: P.C hasChild.Doctor
• Existential restriction: P.C hasChild.Lawyer
• Maximum cardinality: nP.C 3hasChild.Doctor
• Minimum cardinality: nP.C 1hasChild.Male
• Specific Value: P.{x} hasColleague.{Matthew}

• Nesting of constructors can be arbitrarily complex
• Person hasChild.(Doctor hasChild.Doctor)
• Lots of redundancy
• A B is equivalent to ( A B)
• P.C is equivalent to P. C

OWL (1.0 and 1.1)

February 2004

Web Ontology Language

Built on top of RDF(S)

Three layers:
- OWL Lite
- A small subset of primitives
- Easier for frame-based tools to transition to
- OWL DL
- Description logic
- Decidable reasoning
- OWL Full
- RDF extension, allows metaclasses

Several syntaxes:
- Abstract syntax
- Manchester syntax
- RDF/XML

OWL 2 (I). New features

• October 2009

• New features
• Syntactic sugar
• Disjoint union of classes
• New expressivity
• Keys
• Property chains
• Richer datatypes, data ranges
• Qualified cardinality restrictions
• Asymmetric, reflexive, and disjoint properties
• Enhanced annotation capabilities

• New syntax
• OWL2 Manchester syntax

OWL 2 (II). Three new profiles

• OWL2 EL
• Ontologies that define very large numbers of classes and/or properties,
• Ontology consistency, class expression subsumption, and instance
checking can be decided in polynomial time.
• OWL2 QL
• Sound and complete query answering is in LOGSPACE (more precisely, in
AC0) with respect to the size of the data (assertions),
• Provides many of the main features necessary to express conceptual
models (UML class diagrams and ER diagrams).
• It contains the intersection of RDFS and OWL 2 DL.
• OWL2 RL
• Inspired by Description Logic Programs and pD*.
• Syntactic subset of OWL 2 which is amenable to implementation using rule-
based technologies, and presenting a partial axiomatization of the OWL 2
RDF-Based Semantics in the form of first-order implications that can be
used as the basis for such an implementation.
• Scalable reasoning without sacrificing too much expressive power.
• Designed for
• OWL applications trading the full expressivity of the language for
efficiency,
• RDF(S) applications that need some added expressivity from OWL 2.

OWL: Most common constructors

Intersection: C1 ... Cn intersectionOf Human Male
Union: C1 ... Cn unionOf Doctor Lawyer
Negation: C complementOf Male
Nominals: {x1} ... {xn} oneOf {john} ... {mary}
Universal restriction: P.C allValuesFrom hasChild.Doctor
Existential restriction: P.C someValuesFrom hasChild.Lawyer
Maximum cardinality: nP[.C] maxCardinality (qualified or not) 3hasChild[.Doctor]
Minimum cardinality: nP[.C] minCardinality (qualified or not) 1hasChild[.Male]
Exact cardinality: =nP[.C] exactCardinality (qualified or not) =1hasMother[.Female]
Specific Value: P.{x} hasValue hasColleague.{Matthew}
Local reflexivity: -- hasSelf Narcisist Person hasSelf(loves)

Keys -- hasKey hasKey(Person, passportNumber, country)

Subclass C1 C2 subClassOf Human Animal Biped
Equivalence C1 C2 equivalentClass Man Human Male
Disjointness C1 C2 disjointWith, AllDisjointClasses Male Female
DisjointUnion C C1 ... Cn and
Ci Cj forall i≠j disjointUnionOf Person DisjointUnionOf (Man, Woman)

Metaclasses and annotations on axioms are also valid in OWL2, and declarations of classes have to provided.
Full list available in reference specs and in the Quick Reference Guide: http://www.w3.org/2007/OWL/refcard

OWL: Most common constructors

Subproperty P1 P2 subPropertyOf hasDaughter hasChild
Equivalence P1 P2 equivalentProperty cost price
DisjointProperties P1 ... Pn disjointObjectProperties hasDaughter hasSon
Inverse P1 P2- inverseOf hasChild hasParent-
Transitive P+ P TransitiveProperty ancestor+ ancestor
Functional 1P FunctionalProperty T 1hasMother
InverseFunctional 1P- InverseFunctionalProperty T 1hasPassportID-
Reflexive ReflexiveProperty
Irreflexive IrreflexiveProperty
Asymmetric AsymmetricProperty
Property chains P P1 o ... o Pn propertyChainAxiom hasUncle hasFather o hasBroth

Equivalence {x1} {x2} sameIndividualAs {oeg:OscarCorcho} {img:Oscar}
Different {x1} {x2} differentFrom, AllDifferent {john} {peter}

NegativePropertyAssertion NegativeDataPropertyAssertion {hasAge john 35}
NegativeObjectPropertyAssertion {hasChild john peter}

Besides, top and bottom object and datatype properties exist

Basic Inference Tasks

• Subsumption – check knowledge is correct (captures intuitions)
• Does C subsume D w.r.t. ontology O? (in every model I of O, CI DI )
• Equivalence – check knowledge is minimally redundant (no
unintended synonyms)
• Is C equivalent to D w.r.t. O? (in every model I of O, CI = DI )
• Consistency – check knowledge is meaningful (classes can have
instances)
• Is C satisfiable w.r.t. O? (there exists some model I of O s.t. CI )
• Instantiation and querying
• Is x an instance of C w.r.t. O? (in every model I of O, xI CI )
• Is (x,y) an instance of R w.r.t. O? (in every model I of O, (xI,yI) RI )

• All reducible to KB satisfiability or concept satisfiability w.r.t. a KB
• Can be decided using highly optimised tableaux reasoners

Reasoning Tasks. Classification

Main References

W3C OWL Working Group (2009) OWL2 Web Ontology Language Document Overview.
http://www.w3.org/TR/2009/REC-owl2-overview-20091027/
Dean M, Schreiber G (2004) OWL Web Ontology Language Reference. W3C Recommendation.
http://www.w3.org/TR/owl-ref/

Gómez-Pérez, A.; Fernández-López, M.; Corcho, O. Ontological Engineering. Springer Verlag. 2003

Capítulo 4: Ontology languages

Baader F, McGuinness D, Nardi D, Patel-Schneider P (2003)
The Description Logic Handbook: Theory, implementation and applications.
Cambridge University Press, Cambridge, United Kingdom

Jena web site: http://jena.sourceforge.net/
Jena API: http://jena.sourceforge.net/tutorial/RDF_API/
Jena tutorials: http://www.ibm.com/developerworks/xml/library/j-jena/index.html
http://www.xml.com/pub/a/2001/05/23/jena.html

Pellet: http://clarkparsia.com/pellet
RACER: http://www.racer-systems.com/
FaCT++: http://owl.man.ac.uk/factplusplus/
HermIT: http://hermit-reasoner.com/

Ontology Languages
• A large amount of work on Semantic Web has concentrated on
the definition of a collection or “stack” of languages.
• Used to support the representation and use of metadata
• Basic machinery that we can use to represent the extra semantic
information needed for the Semantic Web

SWRL

Inference
OWL

Integration
Integration
RDFS
RDF(S)
Annotation

RDF Reasoning over the information we have
Could be light-weight (taxonomy)
XML Could be heavy-weight (logic-style)

Integrating information sources

Associating metadata to resources (bindings)

The evolution of the Semantic Web
pre-Semantic Web Semantic Web 1.0 Semantic Web 3.0

Semantic Web
Challenge
2004 2008
No standardised formats RDFS, OWL • Cooperation Dynamicity
e.g., (KA)2 • Decentralised change
• Heterogeneity Multimedia

[Semantic | Web]+ Applications (I)

• No definition in Wikipedia… ;-(

• Why [Semantic | Web]+ application?

[Semantic | Web]+ Applications (II)

• Why [Semantic | Web]+ application?
• Most of them are focused on the use of semantics
• In fact, probably it would be better to use
Semantic [Web]* application
• However, many of them are not so Web-oriented
• E.g., very common in data integration approaches
• http://www.readwriteweb.com/archives/10_semantic_
apps_to_watch.php
• A key element [of a Semantic Web App] is that the apps all
try to determine the meaning of text and other data, and
then create connections for users. Besides, data
portability and connectibility are keys to these new
semantic apps - i.e. using the Web as platform.

What is the Web of Linked Data?
• An extension of the current
Web…
• … where informationdata services
and
are given well-defined and explicitly
represented meaning, …
• … so that it can be shared and used
by humans and machines, ...
• ... better enabling them to work in
cooperation

• How?
• Promoting information exchange by
tagging web content with machine
processable descriptions of its
meaning.
• And technologies and infrastructure
to do this
• And clear principles on how to
publish data

What is a Linked Data application

• Again, no definition yet

• Linked Data is a term used to describe a recommended best
practice for exposing, sharing, and connecting pieces of data,
information, and knowledge on the Semantic Web using URIs and
RDF.
• So every element from the definition of SW application applies

Ontologies

Metadata <RDF triple> <RDF triple> <RDF triple>
<RDF triple> <RDF triple> <RDF triple>

The web

Ontologies

Alignments <RDF triple> <RDF triple> <RDF triple>
Onto. - Schema <RDF triple>
<RDF triple> <RDF triple> Metadata
Data Sources <RDF triple> <RDF triple> <RDF triple>

Resources

Annotation-focused applications: key characteristics

• Available at all stages (pre-Semantic Web, SW1.0
and SW3.0), although predominantly in the early
ones
• Single (usually small) ontologies, many of them built
manually
• Centralised ontologies
• Instances stored in a centralised manner, together
with the ontologies, or in separate files/DBs
• Low heterogeneity and relatively small scale
• Homogeneous quality in data

Annotation in the pre-Semantic Web

• (KA)2

Semantic Web Portals
Semantic Driven

Permission-based

User Oriented
Portal Administrators
Ontologies and Software

O2
O1
Oi
Oj
Extranet Users

Agents External resources

Semantic-based Visualisation

Workpackage

has associated

Deliverable
is generated by has Q.A. partner

Organization

Extranet View (RDF lives behind)

Data integration applications: key characteristics

• Available at later stages (SW1.0 and SW3.0).
• Still single (usually small) ontologies, many of them
built manually
• Although sometimes mappings between local and global
ontologies
• Still centralised ontologies
• Instances live in distributed DBs, with a focus on run-
time queries, although also data warehousing
approach
• Medium heterogeneity and medium scale
• Heterogeneous quality in data

Migrating IGN (Instituto Geográfico Nacional) sources

140

IGN Catalogue Integration: Exploitation of Mappings
Cated.
Query: NGN
NC ¿Edif. Religioso
de Soria?
Construcción Rel.

Soria

BCN200 BCN25
Cated.

Edif. Religioso
NS Nieves

Catedral

Cated. Response:
Ig. Sto. Ermita
Catedral Soria
Ig. Sto. Tomás
Catedral Soria Soria
Ermita N.S. Nieves
Soria Catedral Soria

Alignments between ontologies and the DB

Land Fishing Biological Fisheries Vessel types Gear
areas areas entities commodities and size types

R2O R2O R2O R2O R2O R2O
Document Document Document Document Document Document

FAO
FIGIS DB

http://www.fao.org/aims/aos/fi/

Decision support applications: key characteristics

• Again, available at later stages (SW1.0 and SW3.0).
• Still predominantly single (usually small) ontologies,
many of them built manually
• But mostly heavyweight (they are the ones taking decisions)
• Heavy use of logic
• Still centralised ontologies
• Instances may live together with the ontologies, in
distributed DBs, or in separate RDF files/triplestores.
• Annotation phases are common
• Medium heterogeneity and low/medium scale
• Heterogeneous quality in data

Satellite Image Processing

Space
Segment

Ground
Segment
SATELLITE FILES:
DMOP files

Product
files

Comparison between planning and product generation

DMOP_File#(n+1) DMOP#(n+1)_
StartTime File
DMOP_File#n(StartTime) DMOP_File#n(StopTime) (StopTime)

Instr#1
planning ...
DMOP_er
(ORBIT_NUMBER,
ELAPSED_TIME)
Instr#n
(RA_2) DURATION
planning ...

Instr#n(RA_2)
Product
Generation ... PRODUCT_data_gap
...
PRODUCT_FILE PRODUCT_FILE
RA2_CAL_1P RA2_CAL_1P
Start_time Stop_time
Start_time Stop_time
(SENSING_START) (SENSING_STOP)
(SENSING_START) (SENSING_STOP)

Generating files in RDF
FILE ; DMOP (generated by FOS Mission Planning
System)
RECORD fhr RECORD ID
FILENAME="DMOP_SOF__VFOS20060124_103709_00000000_000
01215_20060131_014048_20060202_035846.N1"
DESTINATION="PDCC"
PHASE_START=2 RECORD
CYCLE_START=44 parameters
REL_START_ORBIT=404
ABS_START_ORBIT=20498

ENDRECORD fhr
................................
RECORD dmop_er
RECORD dmop_er_gen_part
RECORD gen_event_params
RECORD parameters
EVENT_TYPE=RA2_MEA
EVENT_ID="RA2_MEA_00000000002063"
corresponding to other
NB_EVENT_PR1=1 RECORD structure.
NB_EVENT_PR3=0
ORBIT_NUMBER=20521 <?xml version='1.0' encoding='ISO-8859-1'?><rdf:RDF
ELAPSED_TIME=623635 xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#'
DURATION=41627862 xmlns:rdfs='http://www.w3.org/2000/01/rdf-schema#'
ENDRECORD gen_event_params xmlns:NS0='http://protege.stanford.edu/kb#'
>
ENDRECORD dmop_er
<rdf:Description rdf:about='http://protege.stanford.edu/kb#10822'>
ENDLIST all_dmop_er <rdf:type rdf:resource='http://protege.stanford.edu/kb#Instrument_mode'/>
ENDFILE <NS0:instrument_mode_id>MS</NS0:instrument_mode_id>
</rdf:Description>
<rdf:Description rdf:about='http://protege.stanford.edu/kb#11224'>
<rdf:type rdf:resource='http://protege.stanford.edu/kb#DMOP_ER'/>
<NS0:event_id>"GOM_OCC_00000000541299"</NS0:event_id>
<NS0:duration rdf:datatype='http://www.w3.org/2001/XMLSchema#int'>53000</NS0:duration>
<NS0:orbit_number rdf:datatype='http://www.w3.org/2001/XMLSchema#int'>20552</NS0:orbit_number>
<NS0:elapsed_time rdf:datatype='http://www.w3.org/2001/XMLSchema#int'>2452293</NS0:elapsed_time>
<NS0:event_type rdf:resource='http://protege.stanford.edu/kb#10713'/>
</rdf:Description>

1 Ontology

1 reference ontology for annotating all files
RDF files are distributed

Distributed Distributed
Metadata for <RDF triple> Metadata for <RDF triple>
Planning files <RDF triple> Product files <RDF triple>
<RDF triple> <RDF triple>

The product
The planning files
files

Planning file
Satellite Use Case (System Infrastructure): S-OGSA Scenario
Product file
server server
GT4 GT4 Store (start-time, stop-time, gen-time, EPR) 8
Germany Italy OverlapChecking
ONTO-DSI ONTO-DSI Service
3 Annotate file
Get file summaries Grid-KP
File directory 2
Spain
Destroy (if
RDF File5
1a Get file names needed)
Upload 9

Select files to be 4
1
annotated
Obtain ontology
Annotation WebDAV
front-end
XML Summary
File WS-DAIOnt
Create 6
2’ Upload XML
Summary file
1 SemanticBinding SatelliteDomain
Ontology
Input Service
criteria

Store7
8
3 Query
MetadataQuery Notify (start-
QUARC-SG client time, stop-time)
JSP Service

Metadata generation process RD Atlas RD
Metadata querying process F F

150

Fraud detection in car insurance

Collaborative SW applications: key characteristics

• Fully-fledged in the last stage (SW3.0).
• Networks of heterogeneous ontologies
• Some of them built manual, some automatically
• Some of the lightweight and some others heavyweight
(although normally not used in a heavyweight form)
• Dynamic finding of ontologies and terms
• Decentralised ontologies (available in URLs or
search engines)
• Distributed instances living anywhere
• Annotation and integration phases are common
• Instances are created by users
• Large heterogeneity (in domains, quality, provenance, forms
– RDF, tags, etc. -, etc.)
• Large scale

GeoBuddies: A pilgrim in St. James’ Way

• Diverse routes for pilgrims

• Self-emergent community of pilgrims
• People that talk about their experiences during the
way
• People that join together in the joy of walking
• Mobile users

• People want to
• Find interesting locations
• Find community services
• Provide information

GeoBuddies: architecture and main themes

• Agile methods for
Web2.0 data integration
• Facebook
• Flickr
• …

• Mobile applications
exploiting user
generated content

• Evolution of
folksonomies and
ontologies

Las anotaciones
se guardan y los
objetos se consolidan
con bases de datos geográficas
y anotaciones existentes
El usuario ve un punto de walk sun
interés y envía una foto
con sus correspondientes
tired
anotaciones cathedral Servidor de
huge anotaciones
peaceful
BBDD geográficas
Motor de recomendaciones
(sólo geográfico)

El usuario quiere saber
qué puntos de interés le
pueden interesar en la zona
en la que se encuentra Servidor de
anotaciones
Motor de recomendaciones (todos los usuarios)
(geográfico + tags + ontologías)

mezcla
Servidor de
ontologías

Camino Personalizado

Catalogue Integration in the Geographical domain
• Monolingual Knowledge bases of IGN
(spanish):
• NC (Nomenclátor Conciso),
• NGN (Nomenclátor Geográfico Nacional),
• BCN200 (Base Cartográfica Nacional
escala 1:200.000),
• BCN25 (Base Cartográfica Nacional
escala 1:25.000)

• Monolingual Knowledge bases of
CC.AA. (spanish, basque, galician):
Castilla y León, Cataluña, Euskadi,
Extremadura, Galicia, La Rioja, Madrid, Murcia,
Navarra.

• Creation of an ontology from IGN
resources and creation of mappings
with IGN knowledge bases

Geobuddies Networks of Ontologies

• Generation of the Phenomen
ontology from IGN
catalogues using linguistic Art Buildings
analysis Ontology

• Art ontologies, Building
ontologies and artistic styles Geographical Community
Organization Ont. Services
built from standardized Ontology

resources
• Community building
ontologies built from Web Core
resources
• Instances are distributed and Personalization
Ontology
Artistic
Styles
kept in their original sources
• Alignments between
ontologies and resources are
first class citizens

When folksonomies meet ontologies

Users annotate with their own tags
-The system provides hints about commonly used tags on a
predictive style (like SMSs)
-Tag clouds can be generated out of this, based on geographical information,
services or in general

Tags are indexed according to ontologies
Predictive tags are enriched with ontologies

Users request information using their own tags
-The system provides hints about commonly used tags on a
predictive style (like SMSs)
-Collaborative filtering techniques can be used to recommend the
most closely-related tags
-Requests can be extended with ontology-based annotations

Reflections: which are the characteristics of these
applications in terms of…?

• Ontologies
• Single versus network of ontologies?
• Are ontologies built from scratch or reusing knowledge-
aware resources?
• Are mappings used for solving conceptual mistmaches?
• Instances
• Where are the data/instances?
• Instances are in the ontology
• Instances are in independent RDF files or databases
• Data are kept in the original sources
• Are instances distributed or centralized?
• Have instances a very high rate of changes?
• Heterogeneous provenance of instances
• Degrees of data quality
• Permissions

Where are the instances?

or

Reflections: which are the characteristics of these
applications in terms of…?

• Amount of semantic markup
• Conceptual Heterogeneity (semantic markup based
on different ontologies)
• Interoperability with other semantic resources
• Open to Web resources
• Open to Web services
• Web 2.0 like
• Mobile devices
• Geo-spatial information

Conclusions
We are moving into a new generation of semantic
applications
• Open to web resources
• Open to semantic resources and Linked Data
• Open to the physical world and having an impact on it.
• (I have not talked too much about this: check at
http://www.semsorgrid4env.eu/)
where …
data integration at large scale and user-generated
annotations are some of the main challenges that are
being faced

and... everything combined with
1. Social communities
2. Mobile devices
3. Ubiquitous computing

Introduction to the Semantic Web

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