by Johannes Hoffart, Fabian M Suchanek, Klaus Berberich, Gerhard Weikum
Abstract:
We present YAGO2, an extension of the YAGO knowledge base, in which entities, facts, and events are anchored in both time and space. YAGO2 is built automatically from Wikipedia, GeoNames, and WordNet. It contains 447 million facts about 9.8 million entities. Human evaluation confirmed an accuracy of 95\% of the facts in YAGO2. In this paper, we present the extraction methodology, the integration of the spatio-temporal dimension, and our knowledge representation SPOTL, an extension of the original SPO-triple model to time and space.
Reference:
YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia (Johannes Hoffart, Fabian M Suchanek, Klaus Berberich, Gerhard Weikum), In Artificial Intelligence, Elsevier, volume 194, 2013.
Bibtex Entry:
@article{hoffart2013yago2,
abstract = {We present YAGO2, an extension of the YAGO knowledge base, in which entities, facts, and events are anchored in both time and space. YAGO2 is built automatically from Wikipedia, GeoNames, and WordNet. It contains 447 million facts about 9.8 million entities. Human evaluation confirmed an accuracy of 95\% of the facts in YAGO2. In this paper, we present the extraction methodology, the integration of the spatio-temporal dimension, and our knowledge representation SPOTL, an extension of the original SPO-triple model to time and space.},
author = {Hoffart, Johannes and Suchanek, Fabian M and Berberich, Klaus and Weikum, Gerhard},
journal = {Artificial Intelligence},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
pages = {28--61},
publisher = {Elsevier},
title = {{YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia}},
volume = {194},
year = {2013}
}