TY - JOUR
T1 - The YAGO-NAGA approach to knowledge discovery
AU - Kasneci, Gjergji
AU - Ramanath, Maya
AU - Suchanek, Fabian
AU - Weikum, Gerhard
PY - 2008/12
Y1 - 2008/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=63749102380&partnerID=8YFLogxK
U2 - 10.1145/1519103.1519110
DO - 10.1145/1519103.1519110
M3 - Article
AN - SCOPUS:63749102380
SN - 0163-5808
VL - 37
SP - 41
EP - 47
JO - SIGMOD Record (ACM Special Interest Group on Management of Data)
JF - SIGMOD Record (ACM Special Interest Group on Management of Data)
IS - 4
ER -