Yago: a Core of Semantic Knowledge Unifying Wordnet and Wikipedia

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.