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 38 articles )

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  1. Fang, Hong: pSPARQL: a querying language for probabilistic RDF data (2019)
  2. Rodosthenous, Christos T.; Michael, Loizos: Web-STAR: A visual web-based IDE for a story comprehension system (2019)
  3. Shih Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque: Pykg2vec: A Python Library for Knowledge Graph Embedding (2019) arXiv
  4. Yan, Jihong; Xu, Chen; Li, Na; Gao, Ming; Zhou, Aoying: Optimizing model parameter for entity summarization across knowledge graphs (2019)
  5. Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard: Quantum machine learning: a classical perspective (2018)
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  7. Wang, Chenguang; Song, Yangqiu; Li, Haoran; Zhang, Ming; Han, Jiawei: Unsupervised meta-path selection for text similarity measure based on heterogeneous information networks (2018)
  8. Zaniolo, Carlo; Gao, Shi; Atzori, Maurizio; Chen, Muhao; Gu, Jiaqi: User-friendly temporal queries on historical knowledge bases (2018)
  9. Tenorth, Moritz; Beetz, Michael: Representations for robot knowledge in the \textscKnowRobframework (2017)
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  18. Wang, William Yang; Mazaitis, Kathryn; Lao, Ni; Cohen, William W.: Efficient inference and learning in a large knowledge base. Reasoning with extracted information using a locally groundable first-order probabilistic logic (2015)
  19. Zhao, Yu; Gao, Sheng; Gallinari, Patrick; Guo, Jun: Knowledge base completion by learning pairwise-interaction differentiated embeddings (2015)
  20. Galbrun, Esther; Kimmig, Angelika: Finding relational redescriptions (2014)

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