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Yago: a core of semantic knowledge
Abstract
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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Research on extracting relational facts from text corpora has led to the emergence of large knowledge bases, such as DBpedia, YAGO, Freebase, and Wolfram Alpha.
“There has also been research on extracting relational facts (e.g., Barack Obama is the president of the USA) from text corpora, which has led to the emergence of large knowledge bases, such as DBpedia 19 [Auer et al. 2007], YAGO 20 [Suchanek et al. 2007] as well as commercial ones, such as Freebase 21 [Bollacker et al. 2007] and Wolfram Alpha 22 .”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Suchanek et al. 2007
Wang et al. 2010 developed the T-YAGO knowledge base, which is an extension of YAGO.
“One of the first works addressing this problem was developed by [Wang et al. 2010] with the T-YAGO knowledge base, which is an extension of YAGO [Suchanek et al. 2007].”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Suchanek et al. 2007
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Research works printed but not linked: 26
- 10.1145/336597.336644 · DOI
- 10.5555/280474 · DOI
- Bunescu R. C., 2006, EACL
- 10.3115/1220575.1220646 · DOI
- Chatterjee N., 2005, Workshop on Modern Approaches in Translation Technologies
- 10.1109/ICDE.2005.125 · DOI
- 10.1145/1014052.1014065 · DOI
- Cunningham H., 2002, ACL
- 10.1145/988672.988687 · DOI
- 10.7551/mitpress/7287.001.0001 · DOI
- Graupmann J., 2005, VLDB
- Horrocks I., 2006, KR
- 10.1007/11871637_24 · DOI
- Kinzler D., 2005, Wikimania
- 10.1145/1008992.1009039 · DOI
- Matuszek C., 2006, AAAI Spring Symposium
- 10.1145/505168.505170 · DOI
- 10.5555/1090488.1090490 · DOI
- 10.3115/1220175.1220190 · DOI
- 10.1007/11428817_7 · DOI
- Russell S., 2002, Prentice Hall
- 10.3115/1220175.1220276 · DOI
- 10.5555/1655829 · DOI
- 10.1145/1150402.1150492 · DOI
- Suchanek F. M., 2006, Workshop on Ontology Population at ACL/COLING
- 10.1007/978-3-540-34963-1_21 · DOI
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Metadata
- Authors
- Suchanek, F. M., Kasneci, G., & Weikum, G.
- Affiliations
- Max-Planck-Institute for Computer Science
- Venue
- Proceedings of the 16th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2007-05-08
- Pages
- 697-706
- Cited by
- 2,512
- References
- 0
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