Introduction to Linked Data
Oscar Corcho, Asunción Gómez Pérez ({ocorcho, asun}@fi.upm.es)
Universidad Politécnica de Madrid
Universidad del Valle, Cali, ColombiaSeptember 10th 2010
Credits: Raúl García Castro, Oscar Muñoz, Jose Angel Ramos Gargantilla, María del Carmen Suárez de Figueroa, Boris Villazón, Alex de León, Víctor Saquicela, Luis Vilches, Miguel Angel García, Manuel Salvadores, Guillermo Alvaro, Juan Sequeda, Carlos Ruiz Moreno and many others
Work distributed under the license Creative Commons Attribution-Noncommercial-Share Alike 3.0
2
Contents
• Introduction to Linked Data• Linked Data publication
- Methodological guidelines for Linked Data publication- RDB2RDF tools- Technical aspects of Linked Data publication
• Linked Data consumption
What is the Web of Linked Data?
• 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
- And clear principles on how to publish data
data
What is Linked Data?
• 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.
- Part of the Semantic Web- Exposing, sharing and connecting data- Technologies: URIs and RDF (although others are also
important)
5
The four principles (Tim Berners Lee, 2006)
1. Use URIs as names for things
2. Use HTTP URIs so that people can look up those names.
3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL)
4. Include links to other URIs, so that they can discover more things.
• http://www.w3.org/DesignIssues/LinkedData.html
http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
Linked Open Data evolution
2007
2008
2009
7
LOD Cloud May 2007
Figure from [4]
Facts:• Focal points:
• DBPedia: RDFized vesion of Wikipiedia; many ingoing and outgoing links
• Music-related datasets• Big datasets include FOAF, US Census data• Size approx. 1 billion triples, 250k links
8
LOD Cloud September 2008
Figure from [4]
Facts:• More than 35 datasets interlinked• Commercial players joined the cloud,
e.g., BBC• Companies began to publish and host
dataset, e.g. OpenLink, Talis, or Garlik.• Size approx. 2 billion triples, 3 million
links
9
LOD Cloud March 2009
Figure from [4]
Facts:• Big part from Linking Open Drug cloud and
the BIO2RDF project (bottom)• Notable new datasets: Freebase,
OpenCalais, ACM/IEEE• Size > 10 billion triples
LOD clouds
Why Linked Data?
• Basically, to move from a Web of documents to a Web of Data
• Let’s try an example:• Tell me which football players, born in the province of
Albacete, in Spain, have scored a goal in the World Cup final
• Disclaimer:- Sorry to use an example about football, but you have to
understand that for several years Spaniards will be talking about football a lot ;-)
Example courtesy of Guillermo Alvaro Rey
Information search in the Web of documents
¿?
Example courtesy of Guillermo Alvaro Rey
What we were actually looking for
Example courtesy of Guillermo Alvaro Rey
It would be better to make a data query…
(football players from Albacete who played Eurocup 2008)
Example courtesy of Guillermo Alvaro Rey
How should we publish data?
• Formats in which data is published nowadays…- XML- HTML- DBs- APIs- CSV- XLS- …
• However, main limitations from a Web of Data point of view- Difficult to integrate- Data is not linked to each other, as it happens with Web
documents.
Which format do we use then?
• RDF (Resource Description Framework)
- Data model- Based on triples: subject, predicate, object
• <Oscar> <vive en> <Madrid>• <Madrid> <es la capital de> <España>• <España> <es campeona de> <Mundial de Fútbol>• …
- Serialised in different formats• RDF/XML, RDFa, N3, Turtle, JSON…
URIs (Universal-Uniform Resource Identifer)
• Two types of identifiers can be used to identify Linked Data resources- 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
- Plain URIs can also be used, as in FOAF:• http://xmlns.com/foaf/0.1/Person
17
18
How do we publish Linked Data?
1. Exposing Relational Databases or other similar formats into Linked Data- D2R- Triplify- R2O- NOR2O- Virtuoso- Ultrawrap- …
2. Using native RDF triplestores- Sesame- Jena- Owlim- Talis platform- …
3. Incorporating it in the form of RDFa in CMSs like Drupal
How do we consume Linked Data?
• Linked Data browsers- To explore things and datasets and to navigate between them.- Tabulator Browser (MIT, USA), Marbles (FU Berlin, DE),
OpenLink RDF Browser (OpenLink, UK), Zitgist RDF Browser (Zitgist, USA), Disco Hyperdata Browser (FU Berlin, DE), Fenfire (DERI, Ireland)
• Linked Data mashups- Sites that mash up (thus combine Linked data)- Revyu.com (KMI, UK), DBtune Slashfacet (Queen Mary, UK),
DBPedia Mobile (FU Berlin, DE), Semantic Web Pipes (DERI, Ireland)
• Search engines- To search for Linked Data.- Falcons (IWS, China), Sindice (DERI, Ireland), MicroSearch
(Yahoo, Spain), Watson (Open University, UK), SWSE (DERI, Ireland), Swoogle (UMBC, USA)
19Listing on this slide by T. Heath, M. Hausenblas, C. Bizer, R. Cyganiak, O. Hartig
Linked Data browsers (Disco)
Linked Data Mashup (LinkedGeoData)
© Migración de datos a la Web de los Datos - Enfoques, técnicas y herramientas Luis Manuel Vilches Blázquez
Linked Data Mashup (DBpedia Mobile)
© Migración de
datos a la Web de los Datos - Enfoqu
es, técnica
s y herramientas
Luis Manuel Vilches Blázqu
ez
http://wiki.dbpedia.org/DBpediaMobile
Linked Data Search Engines (Sindice and SIG.MA)
• Entity lookup service. Find a document that mentions a URI or a keyword.
Linked Data Search Engines (NYT)
• The New York Times: Alumni In The News- http://data.nytimes.com/schools/schools.html
Linked Data Search Engines (NYT)
• The New York Times: Source code is available
• … and is based on SPARQL queries
26
One additional motivation: Open Government
• Government and state administration should be opened at all levels to effective public scrutiny and oversight
• Objectives:- Transparency- Participation- Collaboration- Inclusion
• Cost reduction- Interoperability- Reusability
• Leadership- Market & Value
• Some Links:• B. Obama –Transparency
and Open Government• T. Berners-Lee - Raw data now!• J. Manuel Alonso -
¿Qué es Open Data?• Open Government Data• 8 Principles of Open Government
Data
27
Open Government. USA and UK
TOP-DOWN
BOTTOM-UP
Linked Data Mashup (data.gov)
• Clean Air Status and Trends (CASTNET)- http://data-gov.tw.rpi.edu/demo/exhibit/demo-8-castnet.php
29
Linked Data in the UK
• Education- http://education.data.gov.uk/id/school/106661
• Parliament- http://parliament.psi.enakting.org/id/member/1227
• Maps- E.g., London:
http://data.ordnancesurvey.co.uk/id/7000000000041428- http://map.psi.enakting.org
• Transport- http://www.dft.gov.uk/naptan/
• SameAs service- http://www.sameas.org
• Challenges- http://gov.tso.co.uk/openup/sparql/gov-transport
Linked Data Mashup (data.gov.uk)
• Research Funding Explorer- http://bis.clients.talis.com/
31
Open Government Spain. Euskadi
32
Open Government Spain. Abredatos
33
Open Government Spain. Zaragoza
34
Open Government Spain. Asturias
Linked Data Mashup (Water quality)
• Water quality in Asturias’ beaches- http://datos.fundacionctic.org/sandbox/asturias/playas/
36
Contents
• Introduction to Linked Data• Linked Data publication
- Methodological guidelines for Linked Data publication- RDB2RDF tools- Technical aspects of Linked Data publication
• Linked Data consumption
GeoLinkedData
• It is an open initiative whose aim is to enrich the Web of Data with Spanish geospatial data.
• This initiative has started off by publishing diverse information sources, such as National Geographic Institute of Spain (IGN-E) and National Statistics Institute (INE)
• http://geo.linkeddata.es
Motivation
» 99.171 % English» 0.019 % Spanish
Source:Billion Triples dataset at http://km.aifb.kit.edu/projects/btc-2010/Thanks to Aidan and Richard
The Web of Data is mainly for English speakers
Poor presence of Spanish
Related Work
40
Impact of Geo.linkeddata.es
• Number of triples in Spanish (July 2010): 1.412.248 • Number of triples in Spanish (End August 2010):
21.463.088
Asunción Gómez Pérez
Before geo.linkeddata.es
en 99,1712875
ja 0,463849377
fr 0,05447229
de 0,034225134
pl 0,02532934
it 0,021982542
es 0,019584648
After geo.linkeddata.es
en 94,18744941
es 5,044085342
ja 0,440538697
fr 0,051734793
de 0,032505155
pl 0,024056418
it 0,020877812
Process for Publishing Linked Data on the Web
Identificationof the data
sources
Vocabularydevelopment
Generationof the RDF Data
Publicationof the RDF data
Linking the RDF data
Data cleansing
Enable effective discovery
1. Identification and selection of the data sources
Identificationof the data
sources
Vocabularydevelopment
Generationof the RDF Data
Publicationof the RDF data
Linking the RDF data
Data cleansing
Enable effective discovery
Instituto GeográficoNacional
Instituto Nacionalde Estadística
43
1. Identification and selection of the data sources
• Instituto Geográfico Nacional (Geographic Spanish Institute)- Multilingual (Spanish,
Vasc, Gallician, Catalan)- Conceptualization
mistmatches- Granularity (scale
concept)- Textual information- Particularaties
• Longitude• latitude
• Instituto Nacional de Estadística (Statistic Spanish Institute)• Monolingual• Numerical information• Particularaties
• Geo (textual level)• Temporal
Asunción Gómez Pérez
1. Identification and selection of the data sources
• IGN-E
•
1. Identification and selection of the data sources
Industry Production Index
Province
Year
2. Vocabulary development
Identificationof the data
sources
Vocabularydevelopment
Generationof the RDF Data
Publicationof the RDF data
Linking the RDF data
Data cleansing
Enable effective discovery
http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/#whichvocabs
This
is no
t eno
ugh
2. Vocabulary development
• Features- Lightweight :
• Taxonomies and a few properties- Consensuated vocabularies
• To avoid the mapping problems- Multilingual
• Linked data are multilingual
• The NeOn methodology can help to - Re-enginer Non ontological resources into ontologies
• Pros: use domain terminology already consensuated by domain experts
- Withdraw in heavyweight ontologies those features that you don’t need
- Reuse existing vocabularies
47Asunción Gómez Pérez
Identificationof the data
sources
Vocabularydevelopment
Generationof the RDF Data
Publicationof the RDF data
Linking the RDF data
Data cleansing
Enable effective discovery
48
Knowledge Resources
Non Ontological Resource
Reuse
Non Ontological Resource
Reengineering
2
2
2
Non Ontological Resources
Thesauri
DictionariesGlossaries Lexicons
TaxonomiesClassification
Schemas
O. Localization
9
Ontology Support Activities: Knowledge Acquisition (Elicitation); Documentation; Configuration Management; Evaluation (V&V); Assessment
1,2,3,4,5,6,7,8, 9
Ontological Resource
Reengineering
4
4
4
O. Aligning
O. Merging
Alignments5
5
5
6
6
6
6
3
Ontological Resource
Reuse
3Ontological Resources
O. Repositories and Registries
FlogicRDF(S)
OWL
Ontology Design
Pattern Reuse
7
O. Design Patterns
Ontology Restructuring(Pruning, Extension,
Specialization, Modularization)
8
O. Specification O. Conceptualization O. ImplementationO. Formalization
1
RDF(S)
OWL
FlogicScheduling
Vocabulary development: Specification
• Content requirements: Identify the set of questions that the ontology should answer- Which one are the provinces in Spain?- Where are the beaches?- Where are the reservoirs?- Identify the production index in Madrid- Which one is the city with higher production index?- Give me Madrid latitude and altitude- ….
• Non-content requirements- The ontology must be in the four official Spanish languages
49Asunción Gómez Pérez
2. Lightweight Ontology Development
hasStatisticalData
on
Ontology
Specification
Legend
hydrOntology
4
FAO
FAO Geopolitical ontology
WGS84
4W3C Vocabulary
GML
4GML Specification
O. Statistics
SCOVO
O. Time
W3C Time
hasLat/Long
hasGeometry
hasLat/Long
hasGeometry
hasLocation/isLocated
Thesaurus
UNESCO
4EGM / ERM
GeoNames
…
scv:Dimension
scv:Itemscv:Dataset
WGS84 Geo Positioning:
an RDF vocabulary
hydrographical
phenomena (rivers, lakes,
etc.)
Ontology for OGC
Geography Markup
Language
Vocabulary for instants,
intervals, durations,
etc.
Names and international
code systems for territories
and groups
Following the INSPIRE (INfrastructure for SPatial InfoRmation in Europe) recommendation.hydrOntology,SCOVO, FAO Geopolitcal, WGS84, GML, and Time
Classes 33 33
Object Properties 44 44
Data Properties 318 318
reused
• Objetivos:
- INSPIRE intenta conseguir fuentes armonizadas de Información Geográfica para dar soporte a la formulación, implementación y evaluación de políticas comunitarias (Medio Ambiente, etc).
- Fuentes de Información Geográfica: Bases de datos de los Estados Miembros (UE) a nivel local, regional, nacional e internacional.
Contexto – Directiva INSPIRE
Luis Manuel Vilches Blázquez
INSPIRE - Anexos
Luis Manuel Vilches Blázquez
hydrOntology
• Existencia de gran diversidad de problemas (múltiples fuentes, heterogeneidad de contenido y estructuración, ambigüedad del lenguaje natural, etc.) en la información geográfica.
• Necesidad de un modelo compartido para solventar los problemas de armonización y estructuración de la información hidrográfica.
• hydrOntology es una ontología global de dominio desarrollada conforme a un acercamiento top-down.
- Recubrir la mayoría de los fenómenos representables cartográficamente asociados al dominio hidrográfico.
- Servir como marco de armonización entre los diferentes productores de información geo-espacial en el entorno nacional e internacional.
- Comenzar con los pasos necesarios para obtener una mejor organización y gestión de la información geográfica (hidrográfica).
Luis Manuel Vilches Blázquez
Fuentes
GEMET
Catálogos de fenómenos
BCN25
BCN200
EGM & ERM CC.AA.
Nomenclátor Geográfico Nacional
Tesauros y Bibliografía
WFD
Nomenclátor Conciso
Diccionarios yMonografías
FTT ADL Getty
Luis Manuel Vilches Blázquez
Criterios de estructuración
• Directiva Marco del Agua- Propuesta por Parlamento y Consejo de la UE- Lista de definiciones de fenómenos hidrográficos
• Proyecto SDIGER- Proyecto piloto INSPIRE- Dos cuencas, países e idiomas
• Criterios semánticos- Diccionarios geográficos- Diccionario de la Real Academia de la Lengua- WordNet- Wikipedia- Bibliografía de varias áreas de conocimiento
• Herencia: Estructuración actual de catálogos• Asesoramiento expertos en toponimia del IGN
Luis Manuel Vilches Blázquez
Modelización del dominio hidrográfico Nivel superior
Nivel inferior
Luis Manuel Vilches Blázquez
Implementación & Formalizacón
12
3
4
5
+150 conceptos (classes) , 47 tipos de relaciones (properties)
y 64 tipos de atributos (attribute types)
+ Pellet
Luis Manuel Vilches Blázquez
2. Vocabulary development: HydrOntology
58Asunción Gómez Pérez
3. Generation of RDF
• From the Data sources- Geographic information
(Databases)- Statistic information
(.xsl)- Geospatial information
• Different technologies for RDF generation- Reengineering patterns- R20 and ODEMapster- Annotation tools- Geometry generation
Identificationof the data
sources
Vocabularydevelopment
Generationof the RDF Data
Publicationof the RDF data
Linking the RDF data
Data cleansing
Enable effective discovery
3. Generation of the RDF Data
INE
NOR2O
ODEMapster
IGN
IGN
Geospatial column
Geometry2RDF
3. Generation of the RDF Data / instances
• NOR2O is a software library that implements the transformations proposed by the Patterns for Re-engineering Non-Ontological Resources (PR-NOR). Currently we have 16 PR-NORs.
• PR-NORs define a procedure that transforms a Non-Ontological Resource (NOR) components into ontology elements. http://ontologydesignpatterns.org/
NOR2O
· Classificationschemes
· Thesauri
· Lexicons
NOR2O
FAO Water classification· Classification scheme
· Path enumeration data model· Implemented in a database
Re-engineering Model for NORs
Ontology Forward
Engineering
Implementation
Formalization
Conceptua-
lization
Speci-
fication
Implementation
Design
Con-
ceptual
Transformation
Patterns for Re-engineeringNon-Ontological Resources
(PR-NOR)
Non-Ontological Resource Ontology
Requirements
NOR Reverse
Engineering
RDF(S)
PR-NOR library at the ODP PortalTechnological support
http://ontologydesignpatterns.org/wiki/Submissions:ReengineeringODPs
3. Generation of the RDF Data – NOR2O
Industry Production Index
Province
Year
NOR2O
hydrOntology & databases
NGN1:25.000
multilingüe
3. Generation of the RDF Data – R2O & ODEMapster
• Creation of the R2O Mappings
3. Generation of the RDF Data – Geometry2RDF
Oracle STO UTIL package
SELECT TO_CHAR(SDO_UTIL.TO_GML311GEOMETRY(geometry)) AS Gml311Geometry
FROM "BCN200"."BCN200_0301L_RIO" cWHERE c.Etiqueta='Arroyo'
3. Generation of the RDF Data – Geometry2RDF
3. Generation of the RDF Data – Geometry2RDF
3. Generation of the RDF data – RDF graphs
• IGN INE
• So far- 7 RDF Named Graphs- 1.412.248 triples
BTN25 BCN200 IPI….
http://geo.linkeddata.es/dataset/IGN/BTN25 http://geo.linkeddata.es/dataset/IGN/BCN200 http://geo.linkeddata.es/dataset/INE/IPI
4. Publication of the RDF Data
SPARQL
Pubby
Linked DataHTML
Virtuoso 6.1.0
Pubby 0.3
Including ProvenanceSupport
Identificationof the data
sources
Vocabularydevelopment
Generationof the RDF Data
Publicationof the RDF data
Linking the RDF data
Data cleansing
Enable effective discovery
4. Publication of the RDF Data
4. Publication of the RDF Data - License
• License for GeoLinkedData- Creative Commons Attribution-ShareAlike 3.0 - GNU Free Documentation License
• Each dataset will have its own specific license, IGN, INE, etc.
5. Data cleansing
• Lack of documentation of the IGN datasets• Broken links: Spain, IGN resources• Lack of documentation of the ontology• Missing english and spanish labels• Building a spanish ontology and importing
some concepts of other ontology (in English):- Importing the English ontology. Add annotations
like a Spanish label to them.- Importing the English ontology, creating new
concepts and properties with a Spanish name and map those to the English equivalents.
- Re-declaring the terms of the English ontology that we need (using the same URI as in the English ontology), and adding a Spanish label.
- Creating your own class and properties that model the same things as the English ontology.
Identificationof the data
sources
Vocabularydevelopment
Generationof the RDF Data
Publicationof the RDF data
Linking the RDF data
Data cleansing
Enable effective discovery
5. Data cleansing
• URIs in Spanish• http://geo.linkeddata.es/ontology/Río
- RDF allows UTF-8 characters for URIs- But, Linked Data URIs has to be URLs as well- So, non ASCII-US characters have to be %code
• http://geo.linkeddata.es/ontology/R%C3%ADo
6. Linking of the RDF Data
• Silk - A Link Discovery Framework for the Web of Data
• First set of links: Provinces of Spain- 86% accuracy
GeoLinkedDataDBPedia Geonames
Identificationof the data
sources
Vocabularydevelopment
Generationof the RDF Data
Publicationof the RDF data
Linking the RDF data
Data cleansing
Enable effective discovery
6. Linking of the RDF Data
• http://geo.linkeddata.es/page/Provincia/Granada
77Asunción Gómez Pérez
7. Enable effective discovery
Identificationof the data
sources
Vocabularydevelopment
Generationof the RDF Data
Publicationof the RDF data
Linking the RDF data
Data cleansing
Enable effective discovery
Provinces
Industry Production Index – Capital of Province
Rivers
Beaches
84
Contents
• Introduction to Linked Data• Linked Data publication
- Methodological guidelines for Linked Data publication- RDB2RDF tools- Technical aspects of Linked Data publication
• Linked Data consumption
Ontology-based Access to DBs
1. Build a new ontology from 1 DB schema and 1 DB
2. Align the ontology built with approach 1 with a legacy ontology
3. Align an existing DB with a legacy ontology
a) Massive dump (semantic data warehouse)
b) Query-driven
4. Align an ontology network with n DB schemas and other data sources
a) Massive dump (semantic data warehouse)
b) Query-driven
new ontology
existing ontology
1 23
4
Ontology-based Access to Databases
BDR ModeloRelacional
Personal
Organización
Pregunta: Nombre de los profesores de la universidad
UPM
* Un profesor es una persona cuyo puesto es “docente”
* Una universidad es una organización de tipo “3”
Consulta: valores de la columna nombre de los registros de la tabla Personal para los que el valor de la columna puesto is
“docente” que estén relacionados con al menos un registro de la tabla Organización con el valor “3” en la columna tipo y “UPM” en
la columna nombre.
? Procesado de la consulta de acuerdo a la descripción formal
de correspondencia
Ontología
Profesor
Doctorando
Universidad
Procesador
Align data sources with legacy ontologies
Punto Europeo
PuntoGPS
PuntoAsiatico
=
Aeropuertos
f (Aeropuertos)
Ontología O2Ontología O2
Modelo Relacional M1Modelo Relacional M1
PuntoEuropeo
PuntoEspañol
Estación
CentroComunicaciones
Aeropuerto
=Aeropuerto
f (Aeropuertos)
Ontología O1Ontología O1
RC(O2,M1)RC(O1,M1)
R2O
• is a declarative language to specify mappings between relational data sources and ontologies.
RDB
Relational Model
Persons
Organization <xml>R2O Mapping
</xml>
Ontology
Professor
Student
University
Example: types of mappings needed
Attibute Direct Mapping
Attibute Mapping with transformation(Regular Expression)
Relation Mapping w. Transformation(Regular Expression)
Relation Mapping w. Transformation(Keyword search)
Population example (II)
The Operation element defines a transformation based on a regular
expression to be applied to the database column for extracting property values
Population example (II)
A view maps exactly one concept in the
ontology.
A subset of the columns in the view map a concept in
the ontology.
A subset (selection) of the records of a database view map a concept in the ontology.
A subset of the records of a database view map a concept in the onto. but the selection cannot be made using SQL.
A column in a database view maps directly an attribute or a relation.
A column in a database view maps an attribute or a relation after some transformation.
A set of columns in a database view map an attribute or a relation.
One or more concepts can be extracted from a single data field (not in 1NF).
For concepts...
For attributes...
R2O (Relational-to-Ontology) Language
R2O Basic Syntax
<conceptmap-def name="Customer"> <identified-by> Table key </identified-by>
<uri-as> operation </uri-as> <applies-if> condition </applies-if> <joins-via> expression </joins-via>
<documentation>description …</documentation> <described-by>attributes,relations</described-by>
</conceptmap-def>
<conceptmap-def name="Customer"> <identified-by> Table key </identified-by>
<uri-as> operation </uri-as> <applies-if> condition </applies-if> <joins-via> expression </joins-via>
<documentation>description …</documentation> <described-by>attributes,relations</described-by>
</conceptmap-def>
<attributemap-def name="http://esperonto/ff#Title"> <aftertransform>
<operation oper-id="constant"> <arg-restriction on-param="const-val">
<has-column>fsb_ajut.titol</has-column> </arg-restriction>
</operation> </aftertransform></attributemap-def>
<attributemap-def name="http://esperonto/ff#Title"> <aftertransform>
<operation oper-id="constant"> <arg-restriction on-param="const-val">
<has-column>fsb_ajut.titol</has-column> </arg-restriction>
</operation> </aftertransform></attributemap-def>
<relationmap-def name="http://esperonto/ff#isCandidateFor"> <to-concept name="http://esperonto/ff#FundOpp">
<joins-via> <operation oper-id=“equals">
<arg-restriction on-param="value1"> <has-column>fsb_ajut.id</has-column>
</arg-restriction> <arg-restriction on-param="value2">
<has-column>fsb_candidate.forFund</has-column> </arg-restriction>
</operation> </joins-via>
</relationmap-def>
<relationmap-def name="http://esperonto/ff#isCandidateFor"> <to-concept name="http://esperonto/ff#FundOpp">
<joins-via> <operation oper-id=“equals">
<arg-restriction on-param="value1"> <has-column>fsb_ajut.id</has-column>
</arg-restriction> <arg-restriction on-param="value2">
<has-column>fsb_candidate.forFund</has-column> </arg-restriction>
</operation> </joins-via>
</relationmap-def>
ODEMapster
generates RDF instances from relational instances based on the mapping description expressed in the R2O document
94
Mapping Design
• 3 Mapping Creation Steps
– Load Ontology– Load Database(s)– Create mapping
2 Usage Modes– Online mode (run
time query execution)
– Offline mode (materialized RDF
dump)
101
Contents
• Introduction to Linked Data• Linked Data publication
- Methodological guidelines for Linked Data publication- RDB2RDF tools- Technical aspects of Linked Data publication
• Linked Data consumption
RelFinder: finding relations in Linked Data
• E.g., relations between films- “Pulp Fiction”, “Kill Bill” y “Reservoir Dogs”
Exercise on data.gov.uk
- Public schools in London that contain the word “music”
Exercise: find information in DBPedia
http://dbpedia.org/resource/Darth_Vader)
(etc)
Find ficticious serial killers in DBPedia
Image by http://www.flickr.com/photos/bflv/
105
Designing URI sets for the Public Sector (UK)
• http://www.cabinetoffice.gov.uk/media/301253/puiblic_sector_uri.pdf
106
Asociación Española de Linked Data
Introduction to Linked Data
Oscar Corcho, Asunción Gómez Pérez ({ocorcho, asun}@fi.upm.es)
Universidad Politécnica de Madrid
Universidad del Valle, Cali, ColombiaSeptember 10th 2010
Credits: Raúl García Castro, Oscar Muñoz, Jose Angel Ramos Gargantilla, María del Carmen Suárez de Figueroa, Boris Villazón, Alex de León, Víctor Saquicela, Luis Vilches, Miguel Angel García, Manuel Salvadores, Guillermo Alvaro, Juan Sequeda, Carlos Ruiz Moreno and many others
Work distributed under the license Creative Commons Attribution-Noncommercial-Share Alike 3.0