Difference between revisions of "Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia"
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== Overview == | == Overview == | ||
Authors 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 hasWonPrize). 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. Authors 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, authors show how YAGO can be further extended by state-of-the-art [[information extraction]] techniques. | Authors 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 hasWonPrize). 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. Authors 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, authors show how YAGO can be further extended by state-of-the-art [[information extraction]] techniques. | ||
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| + | == Embed == | ||
| + | === Wikipedia Quality === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Suchanek, Fabian M.; Kasneci, Gjergji; Weikum, Gerhard. (2007). "[[Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia]]".DOI: 10.1145/1242572.1242667. | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === English Wikipedia === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | {{cite journal |last1=Suchanek |first1=Fabian M. |last2=Kasneci |first2=Gjergji |last3=Weikum |first3=Gerhard |title=Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia |date=2007 |doi=10.1145/1242572.1242667 |url=https://wikipediaquality.com/wiki/Yago:_a_Core_of_Semantic_Knowledge_Unifying_Wordnet_and_Wikipedia}} | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === HTML === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Suchanek, Fabian M.; Kasneci, Gjergji; Weikum, Gerhard. (2007). &quot;<a href="https://wikipediaquality.com/wiki/Yago:_a_Core_of_Semantic_Knowledge_Unifying_Wordnet_and_Wikipedia">Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia</a>&quot;.DOI: 10.1145/1242572.1242667. | ||
| + | </nowiki> | ||
| + | </code> | ||
Revision as of 12:04, 17 June 2020
Authors | Fabian M. Suchanek Gjergji Kasneci Gerhard Weikum |
|---|---|
Publication date | 2007 |
DOI | 10.1145/1242572.1242667 |
Links | Original |
Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia - scientific work related to Wikipedia quality published in 2007, written by Fabian M. Suchanek, Gjergji Kasneci and Gerhard Weikum.
Overview
Authors 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 hasWonPrize). 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. Authors 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, authors show how YAGO can be further extended by state-of-the-art information extraction techniques.
Embed
Wikipedia Quality
Suchanek, Fabian M.; Kasneci, Gjergji; Weikum, Gerhard. (2007). "[[Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia]]".DOI: 10.1145/1242572.1242667.
English Wikipedia
{{cite journal |last1=Suchanek |first1=Fabian M. |last2=Kasneci |first2=Gjergji |last3=Weikum |first3=Gerhard |title=Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia |date=2007 |doi=10.1145/1242572.1242667 |url=https://wikipediaquality.com/wiki/Yago:_a_Core_of_Semantic_Knowledge_Unifying_Wordnet_and_Wikipedia}}
HTML
Suchanek, Fabian M.; Kasneci, Gjergji; Weikum, Gerhard. (2007). "<a href="https://wikipediaquality.com/wiki/Yago:_a_Core_of_Semantic_Knowledge_Unifying_Wordnet_and_Wikipedia">Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia</a>".DOI: 10.1145/1242572.1242667.
