YAGO2: exploring and querying world knowledge in time, space, context, and many languages

By Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, Edwin Lewis-Kelham, Gerard de Melo, Gerhard Weikum. 2011

376 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

KILT: a Benchmark for Knowledge Intensive Language Tasks (2020, methodology): “, 2011b) supplements the CoNLL 2003 dataset (Sang and De Meulder, 2003) with Wikipedia URL annotations for all entities using the YAGO2 system (Hoffart et al., 2011a).”
Information Extraction over Structured Data: Question Answering with Freebase (2014, background): “, 2008) and Yago2 (Hoffart et al., 2011), it has become more practical to consider answering questions across wider domains, with commercial systems including Google Now, based on Google’s Knowledge Graph, and Facebook Graph Search, based on social network connections.”
Knowledge Graphs (2020, neutral): “…[90], DBpedia [76], Freebase [14], Wikidata [138], YAGO [55], and so on— cover many domains, offer multilingual lexicalisations (e.g., names, aliases, and descriptions of entities) and are either extracted from sources such as Wikipedia [55, 76, 90] or built by communities of volunteers [14, 138].”
Freebase QA: Information Extraction or Semantic Parsing? (2014, background): “, 2008) and Yago2 (Hoffart et al., 2011), has drawn significant interest from both knowledge base (KB) and semantic parsing (SP) researchers.”
AIDA (2011, methodology): “Our approach leverages the YAGO2 knowledge base [8], as an entity catalog and a rich source of relationships among entities.”
PATTY: A Taxonomy of Relational Patterns with Semantic Types (2012, background): “NELL (Carlson 2010) is based on a fixed set of prespecified relations with type signatures, (e.g., personHasCitizenship: 〈person〉 × 〈country〉), and learns to extract suitable noun-phrase pairs from a large Web corpus.”
Deriving Validity Time in Knowledge Graph (2018, background): “YAGO [6] is another earlier example, in which both time and space scopes were extracted using linguistic extraction rules, followed but conflict resolving post-processing.”
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network (2012, background): “, large amounts of facts about entities (UWN/MENTA, based on its integration with YAGO2 [50]), and explicit semantic relations between concepts denoted by Wikipedia categories (WikiNet).”
Natural Language Questions for the Web of Data (2012, methodology): “The ILP harnesses the richness of large knowledge bases like Yago2 (Hoffart et al., 2011b), which has information not only about entities and relations, but also about surface names and textual patterns by which web sources refer to them.”
PIDGIN: ontology alignment using web text as interlingua (2013, methodology): “We conduct our experiments on several large scale opendomain publicly available real-world KBs, namely NELL [8] (a large scale KB extracted automatically from web text), Yago2 [11] (a large scale KB extracted automatically from semi-structured text of Wikipedia infoboxes), Freebase [6] (a large scal”
Language-Aware Truth Assessment of Fact Candidates (2014, background): “This is not the case for other options such as (Auer et al., 2007; Bollacker et al., 2008; Hoffart et al., 2011).”
YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames (2016, background): “Most notably, the facts are checked for compliance with the type signatures of the relations [23,15,11,12,2].”