Browse wiki

Jump to: navigation, search
The YAGO-NAGA approach to knowledge discovery
Abstract This paper gives an overview on the {YAGO-This paper gives an overview on the {YAGO-NAGA} approach to information extraction for building a conveniently searchable, large-scale, highly accurate knowledge base of common facts. {YAGO} harvests infoboxes and category names of Wikipedia for facts about individual entities, and it reconciles these with the taxonomic backbone of {WordNet} in order to ensure that all entities have proper classes and the class system is consistent. Currently, the {YAGO} knowledge base contains about 19 million instances of binary relations for about 1.95 million entities. Based on intensive sampling, its accuracy is estimated to be above 95 percent. The paper presents the architecture of the {YAGO} extractor toolkit, its distinctive approach to consistency checking, its provisions for maintenance and further growth, and the query engine for {YAGO}, coined {NAGA.} It also discusses ongoing work on extensions towards integrating fact candidates extracted from natural-language text sources.racted from natural-language text sources.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Comments YAGO harvests infoboxes and category names of Wikipedia for facts about individual entities, and it reconciles these with the taxonomic backbone of WordNet in order to ensure that all entities have proper classes and the class system is consistent.
Conclusion The YAGO knowledge base represents all facThe YAGO knowledge base represents all facts in the form of unary and binary relations: classes of individual entities, and pairs of entities connected by specific relationship types. This data model can be seen as a typed graph with entities and classes corresponding to nodes and relations corresponding to edges. It can also be interpreted as a collection of RDF triples with two adjacent nodes and their connecting edge denoting a (subject, predicate, object) triple.ing a (subject, predicate, object) triple.
Data source Wikipedia pages  +
Doi 10.1145/1519103.1519110 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22The%2BYAGO-NAGA%2Bapproach%2Bto%2Bknowledge%2Bdiscovery%22  +
Has author Gjergji Kasneci + , Maya Ramanath + , Fabian M. Suchanek + , Gerhard Weikum +
Has domain Computer science +
Has topic Information extraction +
Issue 4  +
Pages 41-47  +
Peer reviewed Yes  +
Publication type Journal article  +
Published in SIGMOD Record +
Research design Design science  +
Research questions This paper gives an overview on the YAGO-NThis paper gives an overview on the YAGO-NAGA approach to information extraction for building a conveniently searchable, large-scale, highly accurate knowledge base of common facts. The paper presents the architecture of the YAGO extractor toolkit, its distinctive approach to consistency checking, its provisions for maintenance and further growth, and the query engine for YAGO, coined NAGA. It also discusses ongoing work on extensions towards integrating fact candidates extracted from natural-language text sources.racted from natural-language text sources.
Revid 11,152  +
Theories Undetermined
Theory type Design and action  +
Title The YAGO-NAGA approach to knowledge discovery
Unit of analysis Article  +
Url http://dx.doi.org/10.1145/1519103.1519110  +
Volume 37  +
Wikipedia coverage Other  +
Wikipedia data extraction Live Wikipedia  +
Wikipedia language Not specified  +
Wikipedia page type Other  +
Year 2008  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:31:47  +
Categories Information extraction  + , Computer science  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 February 2014 16:30:50  +
hide properties that link here 
  No properties link to this page.
 

 

Enter the name of the page to start browsing from.