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

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

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)

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

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

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

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

What we were actually looking for

It would be better to make a data query…

(football players from Albacete who played Eurocup 2008)

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

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)

Linked Data Mashup (DBpedia Mobile)

datos a la Web de los Datos - Enfoqu

es, técnica

s y herramientas

Luis Manuel Vilches Blázqu

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

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

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

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/

Open Government Spain. Euskadi

Open Government Spain. Abredatos

Open Government Spain. Zaragoza

Open Government Spain. Asturias

Linked Data Mashup (Water quality)

• Water quality in Asturias’ beaches- http://datos.fundacionctic.org/sandbox/asturias/playas/

Contents

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

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

sources

Data cleansing

Instituto GeográficoNacional

Instituto Nacionalde Estadística

• 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

• IGN-E

Industry Production Index

Province

2. Vocabulary development

sources

Data cleansing

http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/#whichvocabs

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

sources

Data cleansing

Knowledge Resources

Non Ontological Resource

Reengineering

Non Ontological Resources

Thesauri

DictionariesGlossaries Lexicons

TaxonomiesClassification

Schemas

O. Localization

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

O. Aligning

O. Merging

Alignments5

Ontological Resource

3Ontological Resources

O. Repositories and Registries

FlogicRDF(S)

Ontology Design

Pattern Reuse

O. Design Patterns

Ontology Restructuring(Pruning, Extension,

Specialization, Modularization)

O. Specification O. Conceptualization O. ImplementationO. Formalization

RDF(S)

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

2. Lightweight Ontology Development

hasStatisticalData

Ontology

Specification

Legend

hydrOntology

FAO Geopolitical ontology

4W3C Vocabulary

4GML Specification

O. Statistics

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,

Ontology for OGC

Geography Markup

Language

Vocabulary for instants,

intervals, durations,

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

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).

Fuentes

Catálogos de fenómenos

BCN200

EGM & ERM CC.AA.

Nomenclátor Geográfico Nacional

Tesauros y Bibliografía

Nomenclátor Conciso

Diccionarios yMonografías

FTT ADL Getty

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

Modelización del dominio hidrográfico Nivel superior

Nivel inferior

Implementación & Formalizacón

+150 conceptos (classes) , 47 tipos de relaciones (properties)

y 64 tipos de atributos (attribute types)

+ Pellet

2. Vocabulary development: HydrOntology

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

sources

Data cleansing

3. Generation of the RDF Data

ODEMapster

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/

· Classificationschemes

· Thesauri

· Lexicons

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

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

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 – 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

Linked DataHTML

Virtuoso 6.1.0

Pubby 0.3

Including ProvenanceSupport

sources

Data cleansing

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.

sources

Data cleansing

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

sources

Data cleansing

6. Linking of the RDF Data

• http://geo.linkeddata.es/page/Provincia/Granada

7. Enable effective discovery

sources

Data cleansing

http://geo.linkeddata.es/

Provinces

Industry Production Index – Capital of Province

Rivers

Beaches

Contents

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

Ontology-based Access to Databases

BDR ModeloRelacional

Personal

Organización

Pregunta: Nombre de los profesores de la universidad

* 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)

• is a declarative language to specify mappings between relational data sources and ontologies.

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>

<has-column>fsb_ajut.titol</has-column> </arg-restriction>

</operation> </aftertransform></attributemap-def>

<attributemap-def name="http://esperonto/ff#Title"> <aftertransform>

<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

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

Contents

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)

Find ficticious serial killers in DBPedia

Image by http://www.flickr.com/photos/bflv/

Designing URI sets for the Public Sector (UK)

• http://www.cabinetoffice.gov.uk/media/301253/puiblic_sector_uri.pdf

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

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