YAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia

By Fabian M. Suchanek, Gjergji Kasneci, Gerhard Weikum. 2007

4,714 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Information Retrieval & Search

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Citations in the corpus

QuAC: Question Answering in Context (2018, methodology): “5 We pruned by popularity by selecting articles with at least 100 incoming links, and we additionally removed non-person entities using YAGO (Suchanek et al., 2007).”
SIGMa: simple greedy matching for aligning large knowledge bases (2012, methodology): “Our starting point was the challenging task of aligning the movie database IMDb to the Wikipedia-based YAGO [28].”
Mass-Editing Memory in a Transformer (2022, background): “…hand-curated efforts (Lenat, 1995; Miller, 1995) have been followed by web-powered knowledge graphs (Auer et al., 2007; Bollacker et al., 2007; Suchanek et al., 2007; Havasi et al., 2007; Carlson et al., 2010; Dong et al., 2014; Vrandeˇci´c & Krötzsch, 2014; Bosselut et al., 2019) that…”
Neural Approaches to Conversational AI (2018, neutral): “1 Knowledge Base Organizing the world’s facts and storing them in a structured database, large scale Knowledge Bases (KB) like DBPedia (Auer et al., 2007), Freebase (Bollacker et al., 2008) and Yago (Suchanek et al., 2007) have become important resources for supporting open-domain QA. A typical KB c”
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (2019, methodology): “Previous work has developed models capable of reading and extracting semi-structured text (Suchanek et al., 2007; Hoffart et al., 2013; Auer et al., 2007; Bollacker et al., 2008) and unstructured text (Dong et al.”
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, methodology): “YAGO [91], [92] extracts facts from Wikipedia using classes in WordNet.”
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “The popular knowledge bases include Wikipedia [66], Freebase [37], YAGO [67], DBpedia [68], etc.”
Never-Ending Learning (2015, background): “…coupling constraints C = { 〈 φ k , V k 〉 } among the solutions to these learning tasks, where φ k is a real-valued function over two or more learning tasks, specifying the degree of satisfaction of the constraint, and V k is a vector of indices over learning tasks, specifying the arguments to φ k .”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, background): “Recent years have witnessed a rapid growth of knowledge bases (KBs) such as Freebase 1 , DBPe-dia (Auer et al., 2007), and YAGO (Suchanek et al., 2007).”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “In addition to missing facts, there are many entities that are mentioned on the Web but are currently missing in knowledge graphs like Freebase and YAGO.”
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases (2019, positive): “…using a manually constructed set of patterns (e.g., questions with the phrase “What is the family name of...” are almost always about people), and by attempting to look up the answers in the Yago database (Suchanek et al., 2007) and checking if the answer belongs to a person or location category.”
Knowledge Graph Embedding via Dynamic Mapping Matrix (2015, methodology): “Knowledge Graphs such as WordNet (Miller 1995), Freebase (Bollacker et al. 2008) and Yago (Suchanek et al. 2007) have been playing a pivotal role in many AI applications, such as relation extraction(RE), question answering(Q&A), etc.”
Holographic Embeddings of Knowledge Graphs (2015, background): “A I] 7 D e c 2015 knowledge graph, as they allow to cast the learning task as a problem of supervised representation learning.”
Fast and Accurate Annotation of Short Texts with Wikipedia Pages (2010, background): “Furthermore, we are studying how other by-products of Wikipedia— such as DBpedia.org , Free-base.com , Kylin [18] or YAGO [16]— could be used in Tagme to better relate and/or assign senses to text anchors.”
Deep Text Classification Can be Fooled (2017, background): “In practice, by searching Internet or some databases of facts, e.g., (Suchanek et al. 2007), we can get some facts that are closely related to the insertion point and embody some desirable HTPs of the target class as well.”
Learning to link with wikipedia (2008, background): “Morgan Kaufmann [18] Suchanek, F.M. and Kasneci, G. and Weikum, G. (2007) Yago: a core of semantic knowledge.”
Understandable Big Data: A survey (2015, background): “Some work like YAGO [82] try to have some specific concepts (e.g “American person”), but it seems to us too general in comparison to [77] where concepts such as “NFLTeam”, “GameWinner” or “TeamScoringAll” can be extracted.”
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules (2021, methodology): “In contrast to previous automatic knowledge base construction methods that rely on semi-structured (Suchanek et al., 2007; Hoffart et al., 2013; Auer et al., 2007) and unstructured (Dong et al.”