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KnowNet: A proposal for building highly connected and dense knowledge bases from the web
Cuadros Oller, Montserrat; Rigau Claramunt, German
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
This paper presents a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method uses a wide-coverage and accurate nowledge-based Word Sense Disambiguation algorithm to assign the most appropriate senses to large sets of topically related words acquired from the web. KnowNet, the resulting knowledge-base which connects large sets of semantically-related concepts is a major step towards the autonomous acquisition of knowledge from raw corpora. In fact, KnowNet is several times larger than any available knowledge resource encoding relations between synsets, and the knowledge KnowNet contains outperform any other resource when is empirically evaluated in a common multilingual framework.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
KnowNet
Natural language processing (Computer science)
KYOTO project
Llenguatge natural (Informàtica) -- Processament
Article - Draft
info:eu-repo/semantics/conferenceObject
         

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