DBpedia Version 2014 released

Tuesday, September 9, 2014 - 10:58am

Hi all,

we are happy to announce the release of DBpedia 2014.

The most important improvements of the new release compared to DBpedia 3.9 are:

1. the new release is based on updated Wikipedia dumps dating from April / May 2014 (the 3.9 release was based on dumps from March / April 2013), leading to an overall increase of the number of things described in the English edition from 4.26 to 4.58 million things.

2. the DBpedia ontology is enlarged and the number of infobox to ontology mappings has risen, leading to richer and cleaner data.

The English version of the DBpedia knowledge base currently describes 4.58 million things, out of which 4.22 million are classified in a consistent ontology (http://wiki.dbpedia.org/Ontology2014), including 1,445,000 persons, 735,000 places (including 478,000 populated places), 411,000 creative works (including 123,000 music albums, 87,000 films and 19,000 video games), 241,000 organizations (including 58,000 companies and 49,000 educational institutions), 251,000 species and 6,000 diseases.

We provide localized versions of DBpedia in 125 languages. All these versions together describe 38.3 million things, out of which 23.8 million are localized descriptions of things that also exist in the English version of DBpedia. The full DBpedia data set features 38 million labels and abstracts in 125 different languages, 25.2 million links to images and 29.8 million links to external web pages; 80.9 million links to Wikipedia categories, and 41.2 million links to YAGO categories. DBpedia is connected with other Linked Datasets by around 50 million RDF links.

Altogether the DBpedia 2014 release consists of 3 billion pieces of information (RDF triples) out of which 580 million were extracted from the English edition of Wikipedia, 2.46 billion were extracted from other language editions.

Detailed statistics about the DBpedia data sets in 28 popular languages are provided at Dataset Statistics page (http://wiki.dbpedia.org/Datasets2014/DatasetStatistics).

The main changes between DBpedia 3.9 and 2014 are described below. For additional, more detailed information please refer to the DBpedia Change Log (http://wiki.dbpedia.org/Changelog).

 1. Enlarged Ontology

The DBpedia community added new classes and properties to the DBpedia ontology via the mappings wiki. The DBpedia 2014 ontology encompasses

  • 685  classes (DBpedia 3.9: 529)
  • 1,079 object properties (DBpedia 3.9: 927)
  • 1,600 datatype properties (DBpedia 3.9: 1,290)
  • 116 specialized datatype properties (DBpedia 3.9: 116)
  • 47 owl:equivalentClass and 35 owl:equivalentProperty mappings to http://schema.org

2. Additional Infobox to Ontology Mappings

The editors community of the mappings wiki also defined many new mappings from Wikipedia templates to DBpedia classes. For the DBpedia 2014 extraction, we used 4,339 mappings (DBpedia 3.9: 3,177 mappings), which are distributed as follows over the languages covered in the release.

  • English: 586 mappings
  • Dutch: 469 mappings
  • Serbian: 450 mappings
  • Polish: 383 mappings
  • German: 295 mappings
  • Greek: 281 mappings
  • French: 221 mappings
  • Portuguese: 211 mappings
  • Slovenian: 170 mappings
  • Korean: 148 mappings
  • Spanish: 137 mappings
  • Italian: 125 mappings
  • Belarusian: 125 mappings
  • Hungarian: 111 mappings
  • Turkish: 91 mappings
  • Japanese: 81 mappings
  • Czech: 66 mappings
  • Bulgarian: 61 mappings
  • Indonesian: 59 mappings
  • Catalan: 52 mappings
  • Arabic: 52 mappings
  • Russian: 48 mappings
  • Basque: 37 mappings
  • Croatian: 36 mappings
  • Irish: 17 mappings
  • Wiki-Commons: 12 mappings
  • Welsh: 7 mappings
  • Bengali: 6 mappings
  • Slovak: 2 Mappings

3. Extended Type System to cover Articles without Infobox

 Until the DBpedia 3.8 release, a concept was only assigned a type (like person or place) if the corresponding Wikipedia article contains an infobox indicating this type. Starting from the 3.9 release, we provide type statements for articles without infobox that are inferred based on the link structure within the DBpedia knowledge base using the algorithm described in Paulheim/Bizer 2014 (http://www.heikopaulheim.com/documents/ijswis_2014.pdf). For the new release, an improved version of the algorithm was run to produce type information for 400,000 things that were formerly not typed. A similar algorithm (presented in the same paper) was used to identify and remove potentially wrong statements from the knowledge base.

 4. New and updated RDF Links into External Data Sources

 We updated the following RDF link sets pointing at other Linked Data sources: Freebase, Wikidata, Geonames and GADM. For an overview about all data sets that are interlinked from DBpedia please refer to http://wiki.dbpedia.org/Interlinking.

Accessing the DBpedia 2014 Release 

 You can download the new DBpedia datasets in RDF format from http://wiki.dbpedia.org/Downloads.
In addition, we provide 
some of the core DBpedia data also in tabular form (CSV and JSON formats) at http://wiki.dbpedia.org/DBpediaAsTables.

 As usual, the new dataset is also available as Linked Data and via the DBpedia SPARQL endpoint at http://dbpedia.org/sparql.


 Lots of thanks to

  1. Daniel Fleischhacker (University of Mannheim) and Volha Bryl (University of Mannheim) for improving the DBpedia extraction framework, for extracting the DBpedia 2014 data sets for all 125 languages, for generating the updated RDF links to external data sets, and for generating the statistics about the new release.
  2. All editors that contributed to the DBpedia ontology mappings via the Mappings Wiki.
  3.  The whole DBpedia Internationalization Committee for pushing the DBpedia internationalization forward.
  4. Dimitris Kontokostas (University of Leipzig) for improving the DBpedia extraction framework and loading the new release onto the DBpedia download server in Leipzig.
  5. Heiko Paulheim (University of Mannheim) for re-running his algorithm to generate additional type statements for formerly untyped resources and identify and removed wrong statements.
  6. Petar Ristoski (University of Mannheim) for generating the updated links pointing at the GADM database of Global Administrative Areas. Petar will also generate an updated release of DBpedia as Tables soon.
  7. Aldo Gangemi (LIPN University, France & ISTC-CNR, Italy) for providing the links from DOLCE to DBpedia ontology.
  8.  Kingsley Idehen, Patrick van Kleef, and Mitko Iliev (all OpenLink Software) for loading the new data set into the Virtuoso instance that serves the Linked Data view and SPARQL endpoint.
  9.  OpenLink Software (http://www.openlinksw.com/) altogether for providing the server infrastructure for DBpedia.
  10. Michael Moore (University of Waterloo, as an intern at the University of Mannheim) for implementing the anchor text extractor and and contribution to the statistics scripts.
  11. Ali Ismayilov (University of Bonn) for implementing Wikidata extraction, on which the interlanguage link generation was based.
  12. Gaurav Vaidya (University of Colorado Boulder) for implementing and running Wikimedia Commons extraction.
  13. Andrea Di Menna, Jona Christopher Sahnwaldt, Julien Cojan, Julien Plu, Nilesh Chakraborty and others who contributed improvements to the DBpedia extraction framework via the source code repository on GitHub.
  14.  All GSoC mentors and students for working directly or indirectly on this release: https://github.com/dbpedia/extraction-framework/graphs/contributors

 The work on the DBpedia 2014 release was financially supported by the European Commission through the project LOD2 – Creating Knowledge out of Linked Data (http://lod2.eu/).

More information about DBpedia is found at http://dbpedia.org/About as well as in the new overview article about the project available at  http://wiki.dbpedia.org/Publications.

Have fun with the new DBpedia 2014 release!


Daniel Fleischhacker, Volha Bryl, and Christian Bizer



DBpedia Spotlight V0.7 released

Monday, July 21, 2014 - 9:58am

DBpedia Spotlight is an entity linking tool for connecting free text to DBpedia through the recognition and disambiguation of entities and concepts from the DBpedia KB.

We are happy to announce Version 0.7 of DBpedia Spotlight, which is also the first official release of the probabilistic/statistical implementation.

More information about as well as updated evaluation results for DBpedia Spotlight V0.7 are found in this paper:

Joachim Daiber, Max Jakob, Chris Hokamp, Pablo N. Mendes: Improving Efficiency and Accuracy in Multilingual Entity ExtractionISEM2013. 

The changes to the statistical implementation include:

  • smaller and faster models through quantization of counts, optimization of search and some pruning
  • better handling of case
  • various fixes in Spotlight and PigNLProc
  • models can now be created without requiring a Hadoop and Pig installation
  • UIMA support by @mvnural
  • support for confidence value

See the release notes at [1] and the updated demos at [4].

Models for Spotlight 0.7 can be found here [2].

Additionally, we now provide the raw Wikipedia counts, which we hope will prove useful for research and development of new models [3].

A big thank you to all developers who made contributions to this version (with special thanks to Faveeo and Idio). Huge thanks to Jo for his leadership and continued support to the community.

Pablo Mendes,

on behalf of Joachim Daiber and the DBpedia Spotlight developer community.

[1] – https://github.com/dbpedia-spotlight/dbpedia-spotlight/releases/tag/release-0.7

[2] – http://spotlight.sztaki.hu/downloads/

[3] – http://spotlight.sztaki.hu/downloads/raw

[4] – http://dbpedia-spotlight.github.io/demo/

(This message is an adaptation of Joachim Daiber’s message to the DBpedia Spotlight list. Edited to suit this broader community and give credit to him.)

Call for Ideas and Mentors for GSoC 2014 DBpedia + Spotlight joint proposal (please contribute within the next days)

Wednesday, February 12, 2014 - 8:32am

We started to draft a document for submission at Google Summer of Code 2014:

We are still in need of ideas and mentors.  If you have any improvements on DBpedia or DBpedia Spotlight that you would like to have done, please submit it in the ideas section now. Note that accepted GSoC students will receive about 5000 USD for a three months, which can help you to estimate the effort and size of proposed ideas. It is also ok to extend/amend existing ideas (as long as you don’t hi-jack them). Please edit here:

Becoming a mentor is also a very good way to get involved with DBpedia. As a mentor you will also be able to vote on proposals, after Google accepts our project. Note that it is also ok, if you are a researcher and have a suitable student to submit an idea and become mentor. After acceptance by Google the student then has to apply for the idea and get accepted.

Please take some time this week to add your ideas and apply as a mentor, if applicable. Feel free to improve the introduction as well and comment on the rest of the document.

Information on GSoC in general can be found here:

Thank you for your help,
Sebastian and Dimitris

Making sense out of the Wikipedia categories (GSoC2013)

Friday, November 29, 2013 - 2:21pm

(Part of our DBpedia+spotlight @ GSoC mini blog series)

Mentor: Marco Fossati @hjfocs <fossati[at]spaziodati.eu>
Student: Kasun Perera <kkasunperera[at]gmail.com>

The latest version of the DBpedia ontology has 529 classes. It is not well balanced and shows a lack of coverage in terms of encyclopedic knowledge representation.

Furthermore, the current typing approach involves a costly manual mapping effort and heavily depends on the presence of infoboxes in Wikipedia articles.

Hence, a large number of DBpedia instances is either un-typed, due to a missing mapping or a missing infobox, or has a too generic or too specialized type, due to the nature of the ontology.

The goal of this project is to identify a set of senseful Wikipedia categories that can be used to extend the coverage of DBpedia instances.

How we used the Wikipedia category system

Wikipedia categories are organized in some kind of really messy hierarchy, which is of little use from an ontological point of view.

We investigated how to process this chaotic world.

Here’s what we have done

We have identified a set of meaningful categories by combining the following approaches:

  1. Algorithmic, programmatically traversing the whole Wikipedia category system.

Wow! This was really the hardest part. Kasun made a great job! Special thanks to the category guru Christian Consonni for shedding light in the darkness of such a weird world.

  1. Linguistic, identifying conceptual categories with NLP techniques.

We got inspired by the YAGO guys.

  1. Multilingual, leveraging interlanguage links.

Kudos to Aleksander Pohl for the idea.

  1. Post-mortem, cleaning out stuff that was still not relevant

No resurrection without Freebase!


We found out a total amount of 3751 candidates that can be used to type the instances.

We produced a dataset in the following format:

<Wikipedia_article_page> rdf:type <article_category>

You can access the full dump here. This has not been validated by humans yet.

If you feel like having a look at it, please tell us what do you think about.

Take a look at the Kasun’s progress page for more details.