YAGO (database)

YAGO (Yet Another Great Ontology) is an open source[3] knowledge base developed at the Max Planck Institute for Informatics in Saarbrücken. It is automatically extracted from Wikipedia and other sources.

YAGO
Developer(s)Max-Planck-Institute Saarbrücken
Initial release2008
Stable release
4.0 / March 2020[1]
Repository
TypeSemantic Web, linked data
LicenseCreative Commons CC-BY 3.0[2]
Websiteyago-knowledge.org

As of 2019, YAGO3 has knowledge of more than 10 million entities and contains more than 120 million facts about these entities.[4] The information in YAGO is extracted from Wikipedia (e.g., categories, redirects, infoboxes), WordNet (e.g., synsets, hyponymy), and GeoNames.[5] The accuracy of YAGO was manually evaluated to be above 95% on a sample of facts.[6] To integrate it to the linked data cloud, YAGO has been linked to the DBpedia ontology[7] and to the SUMO ontology.[8]

YAGO3 is provided in Turtle and tsv formats. Dumps of the whole database are available, as well as thematic and specialized dumps. It can also be queried through various online browsers and through a SPARQL endpoint hosted by OpenLink Software. The source code of YAGO3 is available on GitHub.

YAGO has been used in the Watson artificial intelligence system.[9]

See also

References
  1. "Home: Yago Project".
  2. "Yago Downloads". Retrieved 2015-01-08.
  3. yago3: YAGO is a large semantic knowledge base, derived from Wikipedia, WordNet, WikiData, GeoNames, and other data sources, yago-naga, 2017-08-31, retrieved 2017-08-31
  4. "Yago". Retrieved 2019-01-09.
  5. Fabian M. Suchanek, Gjergji Kasneci and Gerhard Weikum. "Yago - A Core of Semantic Knowledge". 16th international World Wide Web conference (WWW 2007)
  6. "Yago Statistics". Retrieved 2015-01-24.
  7. "Yago Linking". Retrieved 2015-01-24.
  8. "YAGO-SUMO". Retrieved 2012-12-21.
  9. David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A. Kalyanpur, Adam Lally, J. William Murdock, Eric Nyberg, John Prager, Nico Schlaefer, Chris Welty. Building Watson: An Overview of the DeepQA Project. AI Magazine 31(3): 59-79 (2010)


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