Automatic acquisition of hyponyms from large text corpora
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A Thesaurus Construction Method from Large ScaleWeb Dictionaries
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Automatic thesaurus construction
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YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Association thesaurus construction methods based on link co-occurrence analysis for wikipedia
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AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
SemEval-2007 task 10: English lexical substitution task
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FBK-irst: lexical substitution task exploiting domain and syntagmatic coherence
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Computing semantic relatedness using Wikipedia-based explicit semantic analysis
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Ontology Learning and Population: Bridging the Gap between Text and Knowledge - Volume 167 Frontiers in Artificial Intelligence and Applications
LIBSVM: A library for support vector machines
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Automatic thesaurus construction for cross generation corpus
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Acquiring structured data from wikis is a problem of increasing interest in knowledge engineering and Semantic Web. In fact, collaboratively developed resources are growing in time, have high quality and are constantly updated. Among these problems, an area of interest is extracting thesauri from wikis. A thesaurus is a resource that lists words grouped together according to similarity of meaning, generally organized into sets of synonyms. Thesauri are useful for a large variety of applications, including information retrieval and knowledge engineering. Most information in wikis is expressed by means of natural language texts and internal links among Web pages, the so-called wikilinks. In this paper, an innovative method for inducing thesauri from Wikipedia is presented. It leverages on the Wikipedia structure to extract concepts and terms denoting them, obtaining a thesaurus that can be profitably used into applications. This method boosts sensibly precision and recall if applied to re-rank a state-of-the-art baseline approach. Finally, we discuss how to represent the extracted results in RDF/OWL, with respect to existing good practices.