YAGO

YAGO: a core of semantic knowledge. We 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 HASONEPRIZE). 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. 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.


References in zbMATH (referenced in 54 articles )

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  1. Loukachevitch, N. V.; Tikhomirov, M. M.; Parkhomenko, E. A.: Using embedding-based similarities to improve lexical resources (2021)
  2. Voigt, Marco: Decidable (\exists^*\forall^*) first-order fragments of linear rational arithmetic with uninterpreted predicates (2021)
  3. Han, Xiao; Zhang, Chunhong; Guo, Chenchen; Ji, Yang; Hu, Zheng: Distributed representation of knowledge graphs with subgraph-aware proximity (2020)
  4. Han, Yongming; Chen, Guofei; Li, Zhongkun; Geng, Zhiqiang; Li, Fang; Ma, Bo: An asymmetric knowledge representation learning in manifold space (2020)
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  7. Teucke, Andreas; Weidenbach, Christoph: SPASS-AR: a first-order theorem prover based on approximation-refinement into the monadic shallow linear fragment (2020)
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  10. Furbach, Ulrich; Krämer, Teresa; Schon, Claudia: Names are not just sound and smoke: word embeddings for axiom selection (2019)
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  12. Li, Feng-Lin; Chen, Weijia; Huang, Qi; Guo, Yikun: AliMe KBQA: question answering over structured knowledge for E-commerce customer service (2019)
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  14. Shih Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque: Pykg2vec: A Python Library for Knowledge Graph Embedding (2019) arXiv
  15. Tammet, Tanel: GKC: a reasoning system for large knowledge bases (2019)
  16. Wu, Junshuang; Zhang, Richong; Deng, Ting; Huai, Jinpeng: Named entity recognition for open domain data based on distant supervision (2019)
  17. Wu, Peiyun; Zhang, Xiaowang; Feng, Zhiyong: A survey of question answering over knowledge base (2019)
  18. Yang, Juheng; Wang, Zhichun: Cross-lingual entity linking in Wikipedia infoboxes (2019)
  19. Yan, Jihong; Xu, Chen; Li, Na; Gao, Ming; Zhou, Aoying: Optimizing model parameter for entity summarization across knowledge graphs (2019)
  20. Zhang, Lei; Wu, Tianxing; Xu, Liang; Wang, Meng; Qi, Guilin; Sack, Harald: Emerging entity discovery using web sources (2019)

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