Difference between revisions of "Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia"
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| + | | title = Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia | ||
| + | | date = 2007 | ||
| + | | authors = [[Fabian M. Suchanek]]<br />[[Gjergji Kasneci]]<br />[[Gerhard Weikum]] | ||
| + | | doi = 10.1145/1242572.1242667 | ||
| + | | link = http://www2007.org/papers/paper391.pdf | ||
| + | }} | ||
'''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]]. | '''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 == | == 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. | ||
Revision as of 10:56, 26 November 2019
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.
