Deriving a Large-Scale Taxonomy from Wikipedia

By Simone Paolo Ponzetto, Michael Strube. AAAI Conference on Artificial Intelligence, 2007

528 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/22d258fa8f9acadfe513ee8b2842f2bc5db85149

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Citations in the corpus

Question Answering Using Enhanced Lexical Semantic Models (2013, background): “Its abundant concept coverage distinguishes it from other knowledge bases, such as Freebase (Bollacker et al., 2008) and WikiTaxon-omy (Ponzetto and Strube, 2007).”
BabelNet: Building a Very Large Multilingual Semantic Network (2010, unknown): “But while a great deal of work has been recently devoted to the automatic extraction of structured information from Wikipedia (Wu and Weld, 2007; Ponzetto and Strube, 2007; Suchanek et al., 2008; Medelyan et al., 2009, inter alia), the knowledge extracted is organized in a looser way than in a compu”
Automatically refining the wikipedia infobox ontology (2008, background): “Other datasets, like the automatically compiled “YagoMap” or the “Category Ontology” from [26] can also serve for this purpos e”
Extracting implicit knowledge from text (2009, background): “Topics within KA include the discovery of: hypernym relations, e.g., Bambara ndang is-a bow lute (Hearst, 1992; Ponzetto and Strube, 2007); general propositions, e.g., 6However, projects now describable as open domain IE have existed at least as far back as Frump (DeJong, 1982), which were built…”
Weakly-Supervised Acquisition of Open-Domain Classes and Class Attributes from Web Documents and Query Logs (2008, background): “Although some researchers focus on re-organizing or extending classes of instances already available explicitly within manually-built resources such as Wikipedia (Ponzetto and Strube, 2007) or Word-Net (Snow et al., 2006) or both (Suchanek et al., 2007), a large body of previous work focuses on comp”
Autonomously semantifying wikipedia (2007, background): “derived a large scale taxonomy containing subsumption relations based on the category system in Wikipedia [26].”
Open domain question answering using Wikipedia-based knowledge model (2014, methodology): “We applied the method of Ponzetto and Strube (2007) to filter non-isa relations from the upper categories of articles in Korean Wikipedia.”
Understanding user's query intent with wikipedia (2009, methodology): “Bunescu et al. [ 10 ] and Cucerzan et al. [12] explored”
YAGO2: exploring and querying world knowledge in time, space, context, and many languages (2011, background): “Categories and Subject Descriptors H.1 [Information Systems]: Models and Principles General Terms Algorithms Keywords Knowledge Base, Ontology, Temporal, Geo-Spatial, Textual, Multilingual”
Learning to link with wikipedia (2008, background): “More formal reasoning can be made available by taking the (trivial) step of connecting to Wikipedia-derived ontologies such as DBPedia (Auer et al. 2007), Yago (Suchanek et al. 2007), and others (Völkel et al. 2006, Ponzetto and Strube 2007).”
Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches (2019, background): “One popular information source is Wikipedia, which was drawn from in creating common knowledge bases such as YAGO (Suchanek et al., 2007), DBpedia (Auer et al., 2007), WikiTaxonomy (Ponzetto & Strube, 2007).”
PARIS: Probabilistic Alignment of Relations, Instances, and Schema (2011, background): “Notable en-deavors of this kind include DBpedia [2], KnowItAll [10], WikiTaxonomy [26], and yago [30], as well as commercial services such as freebase.com, trueknowledge.com, and wolframalpha.com.”
DELIS Dynamically Evolving , Large-scale Information Systems Integrated Project Member of the FET Proactive Initiative Complex Systems DELIS-TR-594 Yago : A Large Ontology from Wikipedia and WordNet (2007, methodology): “[118] exploit the Wikipedia category network to construct a taxonomy and achieve an precision of around 87%.”
YAGO: A Large Ontology from Wikipedia and WordNet (2008, methodology): “[43], KYLIN [55], and also our own YAGO project (with first results given in [51] and new techniques presented in this article).”