Yago
Top Cited Papers
- 8 May 2007
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 697-706
- https://doi.org/10.1145/1242572.1242667
Abstract
International audienceWe 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 techniquesKeywords
This publication has 14 references indexed in Scilit:
- Combining linguistic and statistical analysis to extract relations from web documentsPublished by Association for Computing Machinery (ACM) ,2006
- EspressoPublished by Association for Computational Linguistics (ACL) ,2006
- Transductive Learning for Text Classification Using Explicit Knowledge ModelsPublished by Springer Nature ,2006
- TopX and XXL at INEX 2005Published by Springer Nature ,2006
- Robust Identification of Fuzzy DuplicatesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- KnowItNowPublished by Association for Computational Linguistics (ACL) ,2005
- Automatic Extraction of Semantic Relationships for WordNet by Means of Pattern Learning from WikipediaPublished by Springer Nature ,2005
- Exploiting dictionaries in named entity extractionPublished by Association for Computing Machinery (ACM) ,2004
- An effective approach to document retrieval via utilizing WordNet and recognizing phrasesPublished by Association for Computing Machinery (ACM) ,2004
- Web-scale information extraction in knowitallPublished by Association for Computing Machinery (ACM) ,2004