YAGO3: A Knowledge Base from Multilingual Wikipedias

By Farzaneh Mahdisoltani, Joanna Asia Biega, Fabian M. Suchanek. 2015

1,028 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Qi Wu
2. Damien Teney
3. Peng Wang
4. Chunhua Shen
5. Anton van den Hengel
6. Zhiyuan Liu
7. Mohit Bansal
8. Zhe Gan

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, methodology): “YAGO [132], [133] extracts facts from Wikipedia using classes in WordNet.”
• Recurrent Event Network for Reasoning over Temporal Knowledge Graphs (2019, background): “The second group of graphs includes WIKI [13] and YAGO [15].”
• Explicit Knowledge-based Reasoning for Visual Question Answering (2015, background): “, YAGO [22, 28], OpenIE [3, 14, 15], NELL [9], NEIL [10, 11]).”
• FVQA: Fact-Based Visual Question Answering (2016, background): “, YAGO [28, 33], OpenIE [24, 34, 35], NELL [26], NEIL [27, 36], WebChild [37,38], ConceptNet [39]).”
• Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision (2018, unknown): “In a large scale setting a meronomy can be automatically extracted from Semantic Web resources like WordNet [34] or Yago [66].”
• Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{docu (2015, methodology): “These have the form: where D and R correspond to the domain and range of the head relation r in YAGO3.”
• The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions (2016, background): “This format of triplets is very general and widely used in large-scale structured knowledge graphs (such as DBpedia [4], Freebase [5], YAGO [18]) to record a surprising variety of information.”
• Cross-lingual Name Tagging and Linking for 282 Languages (2017, background): “Each entry in Wikipedia is also mapped to external knowledge bases such as DBpedia 3 , YAGO (Mahdis-oltani et al., 2015) and Freebase (Bollacker et al., 2008) that contain rich properties.”
• Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs (2019, background): “GDELT ICEWS14 WIKI YAGO MRR H@3 H@10 MRR H@3 H@10 MRR H@3 H@10 MRR H@3 H@10 MRR H@3 H@10”
• Knowledge Graph Embedding for Link Prediction (2020, methodology): “YAGO3-10, sampled from the YAGO3 KG [36], was also proposed by DeŠmers et al.”
• Combining Feature and Instance Attribution to Detect Artifacts (2021, background): “, classifying whether a triple of information is true or false) over the YAGO3-10 knowledge graph corpus (Mahdisoltani et al., 2014).”
• Knowledge graph refinement: A survey of approaches and evaluation methods (2016, methodology): “While DBpedia creates different interlinked knowledge graphs for each language edition of Wikipedia [12], YAGO aims at an automatic fusion of knowledge extracted from various Wikipedia language editions, using different heuristics [64].”
• Visual question answering: A survey of methods and datasets (2016, background): “This led to the development of large-scale Knowledge Bases (KB) such as DBpedia [5], Freebase [7], YAGO [27, 50], OpenIE [6, 17, 18], NELL [10], WebChild [73, 72], and ConceptNet [47].”
• YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames (2016, methodology): “This helped us for YAGO3 [19], which added extraction from 10 di erent Wikipedia languages in 2015.”
• Towards Neural Theorem Proving at Scale (2018, background): “For instance, Freebase (Bollacker et al., 2008) is composed of approximately 637 million facts, while YAGO3 (Mahdisoltani et al., 2015) is composed by approximately 9 million facts.”