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Yago: A Core of Semantic Knowledge Unifying WordNet and Wikipedia

Abstract : We present YAGO, a lightweight 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 heuris-tic 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 cor-rectness 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.
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Fabian Suchanek, Gjergji Kasneci, Gerhard Weikum. Yago: A Core of Semantic Knowledge Unifying WordNet and Wikipedia. 16th international conference on World Wide Web, May 2007, Banff, Canada. pp.697 - 697, ⟨10.1145/1242572.1242667⟩. ⟨hal-01472497⟩



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