SlideShare a Scribd company logo
1 of 23
Download to read offline
An Interlinking-Hub in the Web of Data

Georgi Kobilarov, Chris Bizer, Sören Auer, Jens Lehmann

       Freie Universität Berlin, Universität Leipzig

                                             Georgi Kobilarov, DBpedia at Dublin Core 2008
DBpedia

  DBpedia.org is a community effort to
     extract structured information from Wikipedia
     make this information available on the Web under an open license
     interlink the DBpedia dataset with other open datasets on the Web


  Contributors
     Freie Universität Berlin (Germany)
     Universität Leipzig (Germany)
     OpenLink Software (UK)
     Linking Open Data Community
      (W3C SWEO)




                                                      Georgi Kobilarov, DBpedia at Dublin Core 2008
Extracting Structured Information from Wikipedia

  Wikipedia consists of
     11.2 million articles (2.5 million in English)
     in 264 languages
     monthly growth-rate: 4%

  Wikipedia articles contain structured information
     infoboxes which use a template mechanism
     categorization of the article
     images depicting the article’s topic
     links to external webpages
     intra-wiki links to other articles
     inter-language links to articles about the same topic
      in different languages




                                                        Georgi Kobilarov, DBpedia at Dublin Core 2008
Domain
                                  specific
                                  Data

Title
                                  Images
Description



Languages                         Infoboxes




Web Links

Categorization   Georgi Kobilarov, DBpedia at Dublin Core 2008
Multi-Lingual Abstracts

  The dataset contains a short and a long abstract for each
   concept.
  Short abstracts
     English: 2,490,000
     German: 391,000
     French: 383,000
     Dutch: 284,000
     Polish: 256,000
     Italian: 286,000
     Spanish: 226,000
     Japanese: 199,000
     Portuguese: 246,000
     Swedish: 144,000
     Chinese: 101,000

                                               Georgi Kobilarov, DBpedia at Dublin Core 2008
Infobox Extraction




dbpedia:BBC p:network_name
     „British Broadcasting Corporation (BBC)“

dbpedia:BBC p:country dbpedia:United_Kingdom

dbpedia:BBC p:key_people
dbpedia:Michael_Lyons                      Georgi Kobilarov, DBpedia at Dublin Core 2008
Accessing the DBpedia Dataset over the Web



  1. DB Dumps for Download


  2. SPARQL Endpoint


  3. Linked Data




                                 Georgi Kobilarov, DBpedia at Dublin Core 2008
The DBpedia SPARQL Endpoint



  http://dbpedia.org/sparql


  hosted on a OpenLink Virtuoso server


  can answer SPARQL queries like
     Give me all Sitcoms that are set in NYC?
     All tennis players from Moscow?
     All films by Quentin Tarentino?
     All German musicians that were born in Berlin in the 19th century?
     All soccer players with tricot number 11, playing for a club having a
      stadium with over 40,000 seats and is born in a country with over 10
      million inhabitants?


                                                        Georgi Kobilarov, DBpedia at Dublin Core 2008
Linked Data



  Use URIs as names for things


  Use HTTP URIs so that people can look up those names.


  When someone looks up a URI, provide useful information.


  Include links to other URIs. so that they can discover more
   things.




                                               Georgi Kobilarov, DBpedia at Dublin Core 2008
URIs




           Wikipedia Article URI:
       http://en.wikipedia.org/wiki/BBC


           DBpedia Resource URI
       http://dbpedia.org/resource/BBC



                                Georgi Kobilarov, DBpedia at Dublin Core 2008
W3C Linking Open Data Project




    Community effort to
       publish existing open license datasets as Linked Data on the Web
       interlink things between different data sources




                                                    Georgi Kobilarov, DBpedia at Dublin Core 2008
LOD Datasets on the Web: May 2007




 Over 500 million RDF triples.
                                  Georgi Kobilarov, DBpedia at Dublin Core 2008
LOD Datasets on the Web: April 2008




 Over 2 billion RDF triples.
                                 Georgi Kobilarov, DBpedia at Dublin Core 2008
LOD Datasets on the Web: September 2008




                               Georgi Kobilarov, DBpedia at Dublin Core 2008
Linking Enterprise Data




                          Georgi Kobilarov, DBpedia at Dublin Core 2008
Structuring Wikipedia‘s Knowledge




               Currently under development


            Building a class hierarchy / ontology


      Mapping Wikipedia Templates to DBpedia classes




                                            Georgi Kobilarov, DBpedia at Dublin Core 2008
Class Hierarchy



  Build from scratch
  170 classes
  900 properties


  Structuring actual data, not modeling the world


  No AI terminology, no „living thing“ or „agent“




                                               Georgi Kobilarov, DBpedia at Dublin Core 2008
Template Mapping



            Class TV Episode (Work)


              Wikipedia Templates:
               Television Episode
               UK Office Episode
               Simpsons Episode
                   DoctorWhoBox


                                     Georgi Kobilarov, DBpedia at Dublin Core 2008
Parsers



  Handle Templates Values specifically


  Example: Property splitting
  Person             born         „1.1.1980, [[Berlin]]“


  => split to        birthplace   Berlin
                     birthdate    1980-01-01




                                             Georgi Kobilarov, DBpedia at Dublin Core 2008
Parsers

 Example: Class Rules
 MusicalArtist


 If property „currentMembers“ is set
 => Group


 Otherwise
 => Person




                                       Georgi Kobilarov, DBpedia at Dublin Core 2008
Parsers

 Example: Range Validation


 Google        keypeople
        „[[Eric Schmidt]] ([[CEO]], [[Chairman]]), [[Sergey Brin]],
   [[Larry Page]]


 Company#keyperson range Person#Class


 Googlekeyperson               Eric Schmidt
                               Sergey Brin
                               Larry Page

                                                 Georgi Kobilarov, DBpedia at Dublin Core 2008
Class Hierarchy

  200k people (70k athletes, 65k artists, 18k office holders)
  193k places (100k areas, 40k cities, 10k rivers)
  187k works (71k music albums, 24k singles, 31k films, 15k books)
  87k species
  70k organisations (20k educational institutions, 18k companies,
   12k radio stations)
  22k buildings (8k airports, 5k stations, 2k stadiums, 1k bridges)
  12k planets


  And more… (events, diseases, proteins, drugs, aircrafts,
   automobiles, ships, astronaut, architect, scientists)

                                                 Georgi Kobilarov, DBpedia at Dublin Core 2008
Thanks




         http://dbpedia.org



         georgi.kobilarov@fu-berlin.de



                                     Georgi Kobilarov, DBpedia at Dublin Core 2008

More Related Content

Viewers also liked

DBpedia Day '16 - DBpedia Greek Chapter presentation
DBpedia Day '16 - DBpedia Greek Chapter presentationDBpedia Day '16 - DBpedia Greek Chapter presentation
DBpedia Day '16 - DBpedia Greek Chapter presentationSotiris Karampatakis
 
DBpedia: A Public Data Infrastructure for the Web of Data
DBpedia: A Public Data Infrastructure for the Web of DataDBpedia: A Public Data Infrastructure for the Web of Data
DBpedia: A Public Data Infrastructure for the Web of DataSebastian Hellmann
 
Проект Евросоюза LOD2 и Британский Институт Открытых данных
Проект Евросоюза LOD2 и Британский Институт Открытых данныхПроект Евросоюза LOD2 и Британский Институт Открытых данных
Проект Евросоюза LOD2 и Британский Институт Открытых данныхSören Auer
 
Overview AG AKSW
Overview AG AKSWOverview AG AKSW
Overview AG AKSWSören Auer
 
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataIntroduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataSören Auer
 
DBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, DublinDBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, Dublinm_ackermann
 

Viewers also liked (6)

DBpedia Day '16 - DBpedia Greek Chapter presentation
DBpedia Day '16 - DBpedia Greek Chapter presentationDBpedia Day '16 - DBpedia Greek Chapter presentation
DBpedia Day '16 - DBpedia Greek Chapter presentation
 
DBpedia: A Public Data Infrastructure for the Web of Data
DBpedia: A Public Data Infrastructure for the Web of DataDBpedia: A Public Data Infrastructure for the Web of Data
DBpedia: A Public Data Infrastructure for the Web of Data
 
Проект Евросоюза LOD2 и Британский Институт Открытых данных
Проект Евросоюза LOD2 и Британский Институт Открытых данныхПроект Евросоюза LOD2 и Британский Институт Открытых данных
Проект Евросоюза LOD2 и Британский Институт Открытых данных
 
Overview AG AKSW
Overview AG AKSWOverview AG AKSW
Overview AG AKSW
 
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataIntroduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
 
DBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, DublinDBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, Dublin
 

Similar to An Interlinking Hub in the Web of Data (DBpedia

DBpedia talk at Fjord Berlin
DBpedia talk at Fjord BerlinDBpedia talk at Fjord Berlin
DBpedia talk at Fjord BerlinGeorgi Kobilarov
 
“Library 2.0: Let's get connected!”
“Library 2.0: Let's get connected!”“Library 2.0: Let's get connected!”
“Library 2.0: Let's get connected!”bridgingworlds2008
 
GOKb: Think Global, Act Local (Charleston 2013)
GOKb: Think Global, Act Local (Charleston 2013)GOKb: Think Global, Act Local (Charleston 2013)
GOKb: Think Global, Act Local (Charleston 2013)GOKb Project
 
Wikipedia as source of collaboratively created Knowledge Organization Systems
Wikipedia as source of collaboratively created Knowledge Organization SystemsWikipedia as source of collaboratively created Knowledge Organization Systems
Wikipedia as source of collaboratively created Knowledge Organization SystemsJakob .
 
DBpedia as Gaeilge Chapter
DBpedia as Gaeilge ChapterDBpedia as Gaeilge Chapter
DBpedia as Gaeilge ChapterBianca Pereira
 
Tdwg Ontology 03.Key
Tdwg Ontology 03.KeyTdwg Ontology 03.Key
Tdwg Ontology 03.Keyrogerhyam
 
Freebase: Wikipedia Mining 20080416
Freebase: Wikipedia Mining 20080416Freebase: Wikipedia Mining 20080416
Freebase: Wikipedia Mining 20080416zenkat
 
What is New in W3C land?
What is New in W3C land?What is New in W3C land?
What is New in W3C land?Ivan Herman
 
The European Nucleotide Archive
The European Nucleotide ArchiveThe European Nucleotide Archive
The European Nucleotide ArchiveEBI
 
Library 2.0 and User-Generated Content
Library 2.0 and User-Generated ContentLibrary 2.0 and User-Generated Content
Library 2.0 and User-Generated ContentPatrick Danowski
 
The future importance of bibliographic data
The future importance of bibliographic dataThe future importance of bibliographic data
The future importance of bibliographic dataPatrick Danowski
 
OntoFrame기반 시맨틱 서비스와 서비스 매쉬업
OntoFrame기반 시맨틱 서비스와 서비스 매쉬업OntoFrame기반 시맨틱 서비스와 서비스 매쉬업
OntoFrame기반 시맨틱 서비스와 서비스 매쉬업webscikorea
 
SPARQL1.1 Tutorial, given in UChile by Axel Polleres (DERI)
SPARQL1.1 Tutorial, given in UChile by Axel Polleres (DERI)SPARQL1.1 Tutorial, given in UChile by Axel Polleres (DERI)
SPARQL1.1 Tutorial, given in UChile by Axel Polleres (DERI)net2-project
 
SEMANTIC WEB SOURCES – comparison of open-source Knowledge Graphs
SEMANTIC WEB SOURCES – comparison of open-source Knowledge GraphsSEMANTIC WEB SOURCES – comparison of open-source Knowledge Graphs
SEMANTIC WEB SOURCES – comparison of open-source Knowledge GraphsMatteoBelcao
 
Why libraries should embrace Linked Data
Why libraries should embrace Linked DataWhy libraries should embrace Linked Data
Why libraries should embrace Linked Dataeby
 
GOKb & KB+: An International Partnership to leverage Open Access and Communit...
GOKb & KB+: An International Partnership to leverage Open Access and Communit...GOKb & KB+: An International Partnership to leverage Open Access and Communit...
GOKb & KB+: An International Partnership to leverage Open Access and Communit...Robert H. McDonald
 
Extracting Multilingual Natural-Language Patterns for RDF Predicates
Extracting Multilingual Natural-Language Patterns for RDF PredicatesExtracting Multilingual Natural-Language Patterns for RDF Predicates
Extracting Multilingual Natural-Language Patterns for RDF PredicatesDaniel Gerber
 

Similar to An Interlinking Hub in the Web of Data (DBpedia (20)

DBpedia talk at Fjord Berlin
DBpedia talk at Fjord BerlinDBpedia talk at Fjord Berlin
DBpedia talk at Fjord Berlin
 
“Library 2.0: Let's get connected!”
“Library 2.0: Let's get connected!”“Library 2.0: Let's get connected!”
“Library 2.0: Let's get connected!”
 
GOKb: Think Global, Act Local (Charleston 2013)
GOKb: Think Global, Act Local (Charleston 2013)GOKb: Think Global, Act Local (Charleston 2013)
GOKb: Think Global, Act Local (Charleston 2013)
 
E diplomacy - e-tools
E diplomacy - e-toolsE diplomacy - e-tools
E diplomacy - e-tools
 
Wikipedia as source of collaboratively created Knowledge Organization Systems
Wikipedia as source of collaboratively created Knowledge Organization SystemsWikipedia as source of collaboratively created Knowledge Organization Systems
Wikipedia as source of collaboratively created Knowledge Organization Systems
 
DBpedia as Gaeilge Chapter
DBpedia as Gaeilge ChapterDBpedia as Gaeilge Chapter
DBpedia as Gaeilge Chapter
 
Tdwg Ontology 03.Key
Tdwg Ontology 03.KeyTdwg Ontology 03.Key
Tdwg Ontology 03.Key
 
Freebase: Wikipedia Mining 20080416
Freebase: Wikipedia Mining 20080416Freebase: Wikipedia Mining 20080416
Freebase: Wikipedia Mining 20080416
 
Tel Vortrag
Tel VortragTel Vortrag
Tel Vortrag
 
What is New in W3C land?
What is New in W3C land?What is New in W3C land?
What is New in W3C land?
 
The European Nucleotide Archive
The European Nucleotide ArchiveThe European Nucleotide Archive
The European Nucleotide Archive
 
Library 2.0 and User-Generated Content
Library 2.0 and User-Generated ContentLibrary 2.0 and User-Generated Content
Library 2.0 and User-Generated Content
 
The future importance of bibliographic data
The future importance of bibliographic dataThe future importance of bibliographic data
The future importance of bibliographic data
 
OntoFrame기반 시맨틱 서비스와 서비스 매쉬업
OntoFrame기반 시맨틱 서비스와 서비스 매쉬업OntoFrame기반 시맨틱 서비스와 서비스 매쉬업
OntoFrame기반 시맨틱 서비스와 서비스 매쉬업
 
SPARQL1.1 Tutorial, given in UChile by Axel Polleres (DERI)
SPARQL1.1 Tutorial, given in UChile by Axel Polleres (DERI)SPARQL1.1 Tutorial, given in UChile by Axel Polleres (DERI)
SPARQL1.1 Tutorial, given in UChile by Axel Polleres (DERI)
 
SEMANTIC WEB SOURCES – comparison of open-source Knowledge Graphs
SEMANTIC WEB SOURCES – comparison of open-source Knowledge GraphsSEMANTIC WEB SOURCES – comparison of open-source Knowledge Graphs
SEMANTIC WEB SOURCES – comparison of open-source Knowledge Graphs
 
NISO/DCMI Webinar: Schema.org and Linked Data: Complementary Approaches to Pu...
NISO/DCMI Webinar: Schema.org and Linked Data: Complementary Approaches to Pu...NISO/DCMI Webinar: Schema.org and Linked Data: Complementary Approaches to Pu...
NISO/DCMI Webinar: Schema.org and Linked Data: Complementary Approaches to Pu...
 
Why libraries should embrace Linked Data
Why libraries should embrace Linked DataWhy libraries should embrace Linked Data
Why libraries should embrace Linked Data
 
GOKb & KB+: An International Partnership to leverage Open Access and Communit...
GOKb & KB+: An International Partnership to leverage Open Access and Communit...GOKb & KB+: An International Partnership to leverage Open Access and Communit...
GOKb & KB+: An International Partnership to leverage Open Access and Communit...
 
Extracting Multilingual Natural-Language Patterns for RDF Predicates
Extracting Multilingual Natural-Language Patterns for RDF PredicatesExtracting Multilingual Natural-Language Patterns for RDF Predicates
Extracting Multilingual Natural-Language Patterns for RDF Predicates
 

More from Jakob .

Einheitliche Normdatendienste der VZG
Einheitliche Normdatendienste der VZGEinheitliche Normdatendienste der VZG
Einheitliche Normdatendienste der VZGJakob .
 
Connections that work: Linked Open Data demystified
Connections that work: Linked Open Data demystifiedConnections that work: Linked Open Data demystified
Connections that work: Linked Open Data demystifiedJakob .
 
Linked Open Data in Bibliotheken, Archiven & Museen
Linked Open Data in Bibliotheken, Archiven & MuseenLinked Open Data in Bibliotheken, Archiven & Museen
Linked Open Data in Bibliotheken, Archiven & MuseenJakob .
 
Collaborative Creation of a Wikidata handbook
Collaborative Creation of a Wikidata handbookCollaborative Creation of a Wikidata handbook
Collaborative Creation of a Wikidata handbookJakob .
 
Another RDF Encoding Form
Another RDF Encoding FormAnother RDF Encoding Form
Another RDF Encoding FormJakob .
 
On the Way to a Holding Ontology
On the Way to a Holding OntologyOn the Way to a Holding Ontology
On the Way to a Holding OntologyJakob .
 
Stand und Planungen im Bereich der Schnittstellen in der VZG
Stand und Planungen im Bereich der Schnittstellen in der VZGStand und Planungen im Bereich der Schnittstellen in der VZG
Stand und Planungen im Bereich der Schnittstellen in der VZGJakob .
 
Verwaltung dokumentenorientierter DTDs für den Dokument- und Publikationsserv...
Verwaltung dokumentenorientierter DTDs für den Dokument- und Publikationsserv...Verwaltung dokumentenorientierter DTDs für den Dokument- und Publikationsserv...
Verwaltung dokumentenorientierter DTDs für den Dokument- und Publikationsserv...Jakob .
 
Beschreibung von Bibliotheks-Dienstleistungen mit Mikro-Ontologien
Beschreibung von Bibliotheks-Dienstleistungen mit Mikro-OntologienBeschreibung von Bibliotheks-Dienstleistungen mit Mikro-Ontologien
Beschreibung von Bibliotheks-Dienstleistungen mit Mikro-OntologienJakob .
 
Linking Folksonomies to Knowledge Organization Systems
Linking Folksonomies to Knowledge Organization SystemsLinking Folksonomies to Knowledge Organization Systems
Linking Folksonomies to Knowledge Organization SystemsJakob .
 
Encoding Patron Information in RDF
Encoding Patron Information in RDFEncoding Patron Information in RDF
Encoding Patron Information in RDFJakob .
 
Libraries in a data-centered environment
Libraries in a data-centered environmentLibraries in a data-centered environment
Libraries in a data-centered environmentJakob .
 
Was gibt's wie und wo? Informationen zu Standorten, Exemplaren und Dienstleis...
Was gibt's wie und wo? Informationen zu Standorten, Exemplaren und Dienstleis...Was gibt's wie und wo? Informationen zu Standorten, Exemplaren und Dienstleis...
Was gibt's wie und wo? Informationen zu Standorten, Exemplaren und Dienstleis...Jakob .
 
FRBR light with Simplified Ontology for Bibliographic Resource
FRBR light with Simplified Ontology for Bibliographic ResourceFRBR light with Simplified Ontology for Bibliographic Resource
FRBR light with Simplified Ontology for Bibliographic ResourceJakob .
 
RDF-Daten in eigenen Anwendungen nutzen
RDF-Daten in eigenen Anwendungen nutzenRDF-Daten in eigenen Anwendungen nutzen
RDF-Daten in eigenen Anwendungen nutzenJakob .
 
Linked Data Light - Linkaggregation mit BEACON
Linked Data Light - Linkaggregation mit BEACONLinked Data Light - Linkaggregation mit BEACON
Linked Data Light - Linkaggregation mit BEACONJakob .
 
Revealing digital documents - concealed structures in data
Revealing digital documents - concealed structures in dataRevealing digital documents - concealed structures in data
Revealing digital documents - concealed structures in dataJakob .
 
Wie kommen unsere Sacherschließungsdaten ins Semantic Web? Vom lokalen Normda...
Wie kommen unsere Sacherschließungsdaten ins Semantic Web? Vom lokalen Normda...Wie kommen unsere Sacherschließungsdaten ins Semantic Web? Vom lokalen Normda...
Wie kommen unsere Sacherschließungsdaten ins Semantic Web? Vom lokalen Normda...Jakob .
 
Herausforderungen und Lösungen bei der Publikation und Nutzung von Normdaten ...
Herausforderungen und Lösungen bei der Publikation und Nutzung von Normdaten ...Herausforderungen und Lösungen bei der Publikation und Nutzung von Normdaten ...
Herausforderungen und Lösungen bei der Publikation und Nutzung von Normdaten ...Jakob .
 
Linked Data: Die Zukunft der Nutzung von Katalogdaten
Linked Data: Die Zukunft der Nutzung von KatalogdatenLinked Data: Die Zukunft der Nutzung von Katalogdaten
Linked Data: Die Zukunft der Nutzung von KatalogdatenJakob .
 

More from Jakob . (20)

Einheitliche Normdatendienste der VZG
Einheitliche Normdatendienste der VZGEinheitliche Normdatendienste der VZG
Einheitliche Normdatendienste der VZG
 
Connections that work: Linked Open Data demystified
Connections that work: Linked Open Data demystifiedConnections that work: Linked Open Data demystified
Connections that work: Linked Open Data demystified
 
Linked Open Data in Bibliotheken, Archiven & Museen
Linked Open Data in Bibliotheken, Archiven & MuseenLinked Open Data in Bibliotheken, Archiven & Museen
Linked Open Data in Bibliotheken, Archiven & Museen
 
Collaborative Creation of a Wikidata handbook
Collaborative Creation of a Wikidata handbookCollaborative Creation of a Wikidata handbook
Collaborative Creation of a Wikidata handbook
 
Another RDF Encoding Form
Another RDF Encoding FormAnother RDF Encoding Form
Another RDF Encoding Form
 
On the Way to a Holding Ontology
On the Way to a Holding OntologyOn the Way to a Holding Ontology
On the Way to a Holding Ontology
 
Stand und Planungen im Bereich der Schnittstellen in der VZG
Stand und Planungen im Bereich der Schnittstellen in der VZGStand und Planungen im Bereich der Schnittstellen in der VZG
Stand und Planungen im Bereich der Schnittstellen in der VZG
 
Verwaltung dokumentenorientierter DTDs für den Dokument- und Publikationsserv...
Verwaltung dokumentenorientierter DTDs für den Dokument- und Publikationsserv...Verwaltung dokumentenorientierter DTDs für den Dokument- und Publikationsserv...
Verwaltung dokumentenorientierter DTDs für den Dokument- und Publikationsserv...
 
Beschreibung von Bibliotheks-Dienstleistungen mit Mikro-Ontologien
Beschreibung von Bibliotheks-Dienstleistungen mit Mikro-OntologienBeschreibung von Bibliotheks-Dienstleistungen mit Mikro-Ontologien
Beschreibung von Bibliotheks-Dienstleistungen mit Mikro-Ontologien
 
Linking Folksonomies to Knowledge Organization Systems
Linking Folksonomies to Knowledge Organization SystemsLinking Folksonomies to Knowledge Organization Systems
Linking Folksonomies to Knowledge Organization Systems
 
Encoding Patron Information in RDF
Encoding Patron Information in RDFEncoding Patron Information in RDF
Encoding Patron Information in RDF
 
Libraries in a data-centered environment
Libraries in a data-centered environmentLibraries in a data-centered environment
Libraries in a data-centered environment
 
Was gibt's wie und wo? Informationen zu Standorten, Exemplaren und Dienstleis...
Was gibt's wie und wo? Informationen zu Standorten, Exemplaren und Dienstleis...Was gibt's wie und wo? Informationen zu Standorten, Exemplaren und Dienstleis...
Was gibt's wie und wo? Informationen zu Standorten, Exemplaren und Dienstleis...
 
FRBR light with Simplified Ontology for Bibliographic Resource
FRBR light with Simplified Ontology for Bibliographic ResourceFRBR light with Simplified Ontology for Bibliographic Resource
FRBR light with Simplified Ontology for Bibliographic Resource
 
RDF-Daten in eigenen Anwendungen nutzen
RDF-Daten in eigenen Anwendungen nutzenRDF-Daten in eigenen Anwendungen nutzen
RDF-Daten in eigenen Anwendungen nutzen
 
Linked Data Light - Linkaggregation mit BEACON
Linked Data Light - Linkaggregation mit BEACONLinked Data Light - Linkaggregation mit BEACON
Linked Data Light - Linkaggregation mit BEACON
 
Revealing digital documents - concealed structures in data
Revealing digital documents - concealed structures in dataRevealing digital documents - concealed structures in data
Revealing digital documents - concealed structures in data
 
Wie kommen unsere Sacherschließungsdaten ins Semantic Web? Vom lokalen Normda...
Wie kommen unsere Sacherschließungsdaten ins Semantic Web? Vom lokalen Normda...Wie kommen unsere Sacherschließungsdaten ins Semantic Web? Vom lokalen Normda...
Wie kommen unsere Sacherschließungsdaten ins Semantic Web? Vom lokalen Normda...
 
Herausforderungen und Lösungen bei der Publikation und Nutzung von Normdaten ...
Herausforderungen und Lösungen bei der Publikation und Nutzung von Normdaten ...Herausforderungen und Lösungen bei der Publikation und Nutzung von Normdaten ...
Herausforderungen und Lösungen bei der Publikation und Nutzung von Normdaten ...
 
Linked Data: Die Zukunft der Nutzung von Katalogdaten
Linked Data: Die Zukunft der Nutzung von KatalogdatenLinked Data: Die Zukunft der Nutzung von Katalogdaten
Linked Data: Die Zukunft der Nutzung von Katalogdaten
 

Recently uploaded

Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsMiki Katsuragi
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 

Recently uploaded (20)

DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering Tips
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 

An Interlinking Hub in the Web of Data (DBpedia

  • 1. An Interlinking-Hub in the Web of Data Georgi Kobilarov, Chris Bizer, Sören Auer, Jens Lehmann Freie Universität Berlin, Universität Leipzig Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 2. DBpedia  DBpedia.org is a community effort to  extract structured information from Wikipedia  make this information available on the Web under an open license  interlink the DBpedia dataset with other open datasets on the Web  Contributors  Freie Universität Berlin (Germany)  Universität Leipzig (Germany)  OpenLink Software (UK)  Linking Open Data Community (W3C SWEO) Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 3. Extracting Structured Information from Wikipedia  Wikipedia consists of  11.2 million articles (2.5 million in English)  in 264 languages  monthly growth-rate: 4%  Wikipedia articles contain structured information  infoboxes which use a template mechanism  categorization of the article  images depicting the article’s topic  links to external webpages  intra-wiki links to other articles  inter-language links to articles about the same topic in different languages Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 4. Domain specific Data Title Images Description Languages Infoboxes Web Links Categorization Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 5. Multi-Lingual Abstracts  The dataset contains a short and a long abstract for each concept.  Short abstracts  English: 2,490,000  German: 391,000  French: 383,000  Dutch: 284,000  Polish: 256,000  Italian: 286,000  Spanish: 226,000  Japanese: 199,000  Portuguese: 246,000  Swedish: 144,000  Chinese: 101,000 Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 6. Infobox Extraction dbpedia:BBC p:network_name „British Broadcasting Corporation (BBC)“ dbpedia:BBC p:country dbpedia:United_Kingdom dbpedia:BBC p:key_people dbpedia:Michael_Lyons Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 7. Accessing the DBpedia Dataset over the Web 1. DB Dumps for Download 2. SPARQL Endpoint 3. Linked Data Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 8. The DBpedia SPARQL Endpoint  http://dbpedia.org/sparql  hosted on a OpenLink Virtuoso server  can answer SPARQL queries like  Give me all Sitcoms that are set in NYC?  All tennis players from Moscow?  All films by Quentin Tarentino?  All German musicians that were born in Berlin in the 19th century?  All soccer players with tricot number 11, playing for a club having a stadium with over 40,000 seats and is born in a country with over 10 million inhabitants? Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 9. Linked Data  Use URIs as names for things  Use HTTP URIs so that people can look up those names.  When someone looks up a URI, provide useful information.  Include links to other URIs. so that they can discover more things. Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 10. URIs Wikipedia Article URI: http://en.wikipedia.org/wiki/BBC DBpedia Resource URI http://dbpedia.org/resource/BBC Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 11. W3C Linking Open Data Project  Community effort to  publish existing open license datasets as Linked Data on the Web  interlink things between different data sources Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 12. LOD Datasets on the Web: May 2007  Over 500 million RDF triples. Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 13. LOD Datasets on the Web: April 2008  Over 2 billion RDF triples. Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 14. LOD Datasets on the Web: September 2008 Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 15. Linking Enterprise Data Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 16. Structuring Wikipedia‘s Knowledge Currently under development Building a class hierarchy / ontology Mapping Wikipedia Templates to DBpedia classes Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 17. Class Hierarchy  Build from scratch  170 classes  900 properties  Structuring actual data, not modeling the world  No AI terminology, no „living thing“ or „agent“ Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 18. Template Mapping Class TV Episode (Work) Wikipedia Templates: Television Episode UK Office Episode Simpsons Episode DoctorWhoBox Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 19. Parsers Handle Templates Values specifically Example: Property splitting Person born „1.1.1980, [[Berlin]]“ => split to birthplace Berlin birthdate 1980-01-01 Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 20. Parsers Example: Class Rules MusicalArtist If property „currentMembers“ is set => Group Otherwise => Person Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 21. Parsers Example: Range Validation Google keypeople „[[Eric Schmidt]] ([[CEO]], [[Chairman]]), [[Sergey Brin]], [[Larry Page]] Company#keyperson range Person#Class Googlekeyperson Eric Schmidt Sergey Brin Larry Page Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 22. Class Hierarchy  200k people (70k athletes, 65k artists, 18k office holders)  193k places (100k areas, 40k cities, 10k rivers)  187k works (71k music albums, 24k singles, 31k films, 15k books)  87k species  70k organisations (20k educational institutions, 18k companies, 12k radio stations)  22k buildings (8k airports, 5k stations, 2k stadiums, 1k bridges)  12k planets  And more… (events, diseases, proteins, drugs, aircrafts, automobiles, ships, astronaut, architect, scientists) Georgi Kobilarov, DBpedia at Dublin Core 2008
  • 23. Thanks http://dbpedia.org georgi.kobilarov@fu-berlin.de Georgi Kobilarov, DBpedia at Dublin Core 2008