Campus Event Calendar
Event Entry
What and Who
Ontology Learning
Fabian Suchanek
Ringvorlesung
AG 1, AG 2, AG 3, AG 4, AG 5, SWS
Public Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Thursday, 14 December 2006
13:00
60 Minutes
E1 3 - Hörsaal Gebäude
016
Saarbrücken
Abstract
We present YAGO, a light-weight and extensible ontology with high
coverage and quality.
YAGO builds on entities and relations and currently contains roughly
900,000 entities and 5,000,000 facts.
This includes the Is-A hierarchy as well as non-taxonomic relations
between entities (such as relation{hasWonPrize}).
The facts have been automatically extracted from the unification of
Wikipedia and 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.
Our 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, we show how YAGO can be further extended by state-of-the-art
information extraction techniques.
coverage and quality.
YAGO builds on entities and relations and currently contains roughly
900,000 entities and 5,000,000 facts.
This includes the Is-A hierarchy as well as non-taxonomic relations
between entities (such as relation{hasWonPrize}).
The facts have been automatically extracted from the unification of
Wikipedia and 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.
Our 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, we show how YAGO can be further extended by state-of-the-art
information extraction techniques.
Contact
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Veronika Weinand, 12/12/2006 12:36
Veronika Weinand, 11/21/2006 10:39
Silke Wagner, 11/06/2006 16:17
Veronika Weinand, 11/06/2006 14:39
Veronika Weinand, 11/03/2006 12:21
Veronika Weinand, 10/25/2006 15:08
Veronika Weinand, 10/24/2006 12:47
Veronika Weinand, 10/23/2006 15:06
Veronika Weinand, 10/19/2006 14:54
Veronika Weinand, 10/16/2006 17:07
Veronika Weinand, 11/21/2006 10:39
Silke Wagner, 11/06/2006 16:17
Veronika Weinand, 11/06/2006 14:39
Veronika Weinand, 11/03/2006 12:21
Veronika Weinand, 10/25/2006 15:08
Veronika Weinand, 10/24/2006 12:47
Veronika Weinand, 10/23/2006 15:06
Veronika Weinand, 10/19/2006 14:54
Veronika Weinand, 10/16/2006 17:07