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.

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  1. Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu, Yufeng Huang, Zezhong Xu, Yajing Xu, Ningyu Zhang, Zonggang Yuan, Feiyu Xiong, Huajun Chen: NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs (2022) arXiv
  2. Ceylan, İsmail İlkan; Darwiche, Adnan; Van den Broeck, Guy: Open-world probabilistic databases: semantics, algorithms, complexity (2021)
  3. Confalonieri, Roberto; Weyde, Tillman; Besold, Tarek R.; Moscoso del Prado Martín, Fermín: Using ontologies to enhance human understandability of global post-hoc explanations of black-box models (2021)
  4. Loukachevitch, N. V.; Tikhomirov, M. M.; Parkhomenko, E. A.: Using embedding-based similarities to improve lexical resources (2021)
  5. Voigt, Marco: Decidable (\exists^*\forall^*) first-order fragments of linear rational arithmetic with uninterpreted predicates (2021)
  6. Wei, Shaowei; Yu, Guoxian; Wang, Jun; Domeniconi, Carlotta; Zhang, Xiangliang: Multiple clusterings of heterogeneous information networks (2021)
  7. Han, Xiao; Zhang, Chunhong; Guo, Chenchen; Ji, Yang; Hu, Zheng: Distributed representation of knowledge graphs with subgraph-aware proximity (2020)
  8. Han, Yongming; Chen, Guofei; Li, Zhongkun; Geng, Zhiqiang; Li, Fang; Ma, Bo: An asymmetric knowledge representation learning in manifold space (2020)
  9. Ibrahim Abdelaziz, Julian Dolby, James P. McCusker, Kavitha Srinivas: Graph4Code: A Machine Interpretable Knowledge Graph for Code (2020) arXiv
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  13. Fang, Hong: pSPARQL: a querying language for probabilistic RDF data (2019)
  14. Furbach, Ulrich; Krämer, Teresa; Schon, Claudia: Names are not just sound and smoke: word embeddings for axiom selection (2019)
  15. Joana M. F. da Trindade, Konstantinos Karanasos, Carlo Curino, Samuel Madden, Julian Shun: Kaskade: Graph Views for Efficient Graph Analytics (2019) arXiv
  16. Li, Feng-Lin; Chen, Weijia; Huang, Qi; Guo, Yikun: AliMe KBQA: question answering over structured knowledge for E-commerce customer service (2019)
  17. Rodosthenous, Christos T.; Michael, Loizos: Web-STAR: A visual web-based IDE for a story comprehension system (2019)
  18. Shih Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque: Pykg2vec: A Python Library for Knowledge Graph Embedding (2019) arXiv
  19. Tammet, Tanel: GKC: a reasoning system for large knowledge bases (2019)
  20. Wu, Junshuang; Zhang, Richong; Deng, Ting; Huai, Jinpeng: Named entity recognition for open domain data based on distant supervision (2019)

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