Automatically refining the wikipedia infobox ontology

By Fei Wu, Daniel S. Weld. The Web Conference, 2008

378 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication

View Paper: https://www.semanticscholar.org/paper/0c236e611a90018e84d9de23d1cff241354079be

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Citations in the corpus

Unsupervised Ontology Induction from Text (2010, background): “Recently, ontology learning has attracted increasing interest in both NLP and semantic Web communities (Cimiano, 2006; Maedche, 2002), and a number of machine learning approaches have been developed (e.g., Snow et al. (2006), Cimiano (2006), Suchanek et al. (2008,2009), Wu & Weld (2008)).”
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network (2012, background): “Previous work aimed at transforming its content into a knowledge base includes open-domain relation extraction [81,134, 136], the acquisition of taxonomic [135,108] and other semantic relations [83,34], as well as full-fledged ontologies [124, 12].”
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS (2011, background): “MLN s are an integral part of state-of-the-art approaches in a variety of applications: natural language processing [22], ontology matching [29], information extraction [18], entity resolution [25], etc.”
DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia (2015, methodology): “The KOG system [1] refines existing Wikipedia infoboxes based on machine learning techniques using both SVMs and a more powerful jointinference approach expressed in Markov Logic Networks.”
Recovering Semantics of Tables on the Web (2011, background): “When associated with a class label, the sets of instances may be organized as flat sets or hierarchically, relative to existing hierarchies such as WordNet [25, 26] or the category network within Wikipedia [24, 31].”
Machine Reading at the University of Washington (2010, methodology): “…et al., 2008); KOG learns a rich ontology by combining Wikipedia infoboxes with WordNet via joint inference using Markov Logic Networks (Wu and Weld, 2008), shrinkage over this ontology vastly improves the recall of Kylin’s extractors; UCR performs state-of-the-art unsupervised coreference…”
Machine Reading: A "Killer App" for Statistical Relational AI (2010, methodology): “More recently, machine learning approaches have been developed (e.g., Snow et al. (2006), Cimiano (2006), Suchanek et al. (2008,2009), Wu & Weld (2008)), but they are still limited in several aspects.”
Mining Meaning from Wikipedia (2008, methodology): “Simple pattern matching is improved by the use of parsing [Suchanek et al. 2006], anaphor resolution [Nguyen et al. 2007a, 2007b], selectional constraints [Wang et al. 2007b] and lexical analysis [Wu and Weld 2007, 2008]; however, the exact contribution of each is unclear.”
YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia: Extended Abstract (2013, methodology): “The Kylin/KOG project [56] has developed learning-based methods for automatically typing Wikipedia entities and generating infobox-style facts.”