YAGO: a core of semantic knowledge. We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.

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  1. Zaniolo, Carlo; Gao, Shi; Atzori, Maurizio; Chen, Muhao; Gu, Jiaqi: User-friendly temporal queries on historical knowledge bases (2018)
  2. Tenorth, Moritz; Beetz, Michael: Representations for robot knowledge in the KnowRob framework (2017)
  3. Ciobanu, Gabriel; Horne, Ross; Sassone, Vladimiro: A descriptive type foundation for RDF Schema (2016)
  4. Flati, Tiziano; Vannella, Daniele; Pasini, Tommaso; Navigli, Roberto: MultiWiBi: the multilingual Wikipedia bitaxonomy project (2016)
  5. Alagi, Gábor; Weidenbach, Christoph: NRCL -- a model building approach to the Bernays-Schönfinkel fragment (2015)
  6. Kacfah Emani, Cheikh; Cullot, Nadine; Nicolle, Christophe: Understandable big data: a survey (2015) ioport
  7. Wang, Dong; Zou, Lei; Zhao, Dongyan: Top-$ k$ queries on RDF graphs (2015)
  8. Wang, William Yang; Mazaitis, Kathryn; Lao, Ni; Cohen, William W.: Efficient inference and learning in a large knowledge base. Reasoning with extracted information using a locally groundable first-order probabilistic logic (2015)
  9. Galbrun, Esther; Kimmig, Angelika: Finding relational redescriptions (2014)
  10. Rossetti, Marco; Pareschi, Remo; Stella, Fabio; Arcelli Fontana, Francesca: Integrating concepts and knowledge in large content networks (2014) ioport
  11. Hillenbrand, Thomas; Weidenbach, Christoph: Superposition for bounded domains (2013)
  12. Hoffart, Johannes; Suchanek, Fabian M.; Berberich, Klaus; Weikum, Gerhard: YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia (2013)
  13. Kapur, Deepak; Nieuwenhuis, Robert; Voronkov, Andrei; Weidenbach, Christoph; Wilhelm, Reinhard: Harald Ganzinger’s legacy: contributions to logics and programming (2013)
  14. Korovin, Konstantin: Inst-Gen -- a modular approach to instantiation-based automated reasoning (2013)
  15. Zang, Liang-Jun; Cao, Cong; Cao, Ya-Nan; Wu, Yu-Ming; Cao, Cun-Gen: A survey of commonsense knowledge acquisition (2013)
  16. d’Amato, Claudia; Fanizzi, Nicola; Fazzinga, Bettina; Gottlob, Georg; Lukasiewicz, Thomas: Ontology-based semantic search on the web and its combination with the power of inductive reasoning (2012)
  17. Fietzke, Arnaud; Weidenbach, Christoph: Superposition as a decision procedure for timed automata (2012)
  18. Kruglov, Evgeny; Weidenbach, Christoph: Superposition decides the first-order logic fragment over ground theories (2012)
  19. Navigli, Roberto; Ponzetto, Simone Paolo: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network (2012)
  20. Ranise, Silvio: On the verification of security-aware E-services (2012)

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