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 51 articles )

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  1. Han, Xiao; Zhang, Chunhong; Guo, Chenchen; Ji, Yang; Hu, Zheng: Distributed representation of knowledge graphs with subgraph-aware proximity (2020)
  2. Ibrahim Abdelaziz, Julian Dolby, James P. McCusker, Kavitha Srinivas: Graph4Code: A Machine Interpretable Knowledge Graph for Code (2020) arXiv
  3. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  4. Teucke, Andreas; Weidenbach, Christoph: SPASS-AR: a first-order theorem prover based on approximation-refinement into the monadic shallow linear fragment (2020)
  5. Claudia Schon, Sophie Siebert, Frieder Stolzenburg: Using ConceptNet to Teach Common Sense to an Automated Theorem Prover (2019) arXiv
  6. Fang, Hong: pSPARQL: a querying language for probabilistic RDF data (2019)
  7. Furbach, Ulrich; Krämer, Teresa; Schon, Claudia: Names are not just sound and smoke: word embeddings for axiom selection (2019)
  8. Joana M. F. da Trindade, Konstantinos Karanasos, Carlo Curino, Samuel Madden, Julian Shun: Kaskade: Graph Views for Efficient Graph Analytics (2019) arXiv
  9. Li, Feng-Lin; Chen, Weijia; Huang, Qi; Guo, Yikun: AliMe KBQA: question answering over structured knowledge for E-commerce customer service (2019)
  10. Rodosthenous, Christos T.; Michael, Loizos: Web-STAR: A visual web-based IDE for a story comprehension system (2019)
  11. Shih Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque: Pykg2vec: A Python Library for Knowledge Graph Embedding (2019) arXiv
  12. Tammet, Tanel: GKC: a reasoning system for large knowledge bases (2019)
  13. Wu, Junshuang; Zhang, Richong; Deng, Ting; Huai, Jinpeng: Named entity recognition for open domain data based on distant supervision (2019)
  14. Wu, Peiyun; Zhang, Xiaowang; Feng, Zhiyong: A survey of question answering over knowledge base (2019)
  15. Yang, Juheng; Wang, Zhichun: Cross-lingual entity linking in Wikipedia infoboxes (2019)
  16. Yan, Jihong; Xu, Chen; Li, Na; Gao, Ming; Zhou, Aoying: Optimizing model parameter for entity summarization across knowledge graphs (2019)
  17. Zhang, Lei; Wu, Tianxing; Xu, Liang; Wang, Meng; Qi, Guilin; Sack, Harald: Emerging entity discovery using web sources (2019)
  18. Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard: Quantum machine learning: a classical perspective (2018)
  19. Ma, Zongmin; Li, Guanfeng; Yan, Li: Fuzzy data modeling and algebraic operations in RDF (2018)
  20. Wang, Chenguang; Song, Yangqiu; Li, Haoran; Zhang, Ming; Han, Jiawei: Unsupervised meta-path selection for text similarity measure based on heterogeneous information networks (2018)

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