Ontology learning from Italian legal texts (Contributo in volume (capitolo o saggio))

Type
Label
  • Ontology learning from Italian legal texts (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.3233/978-1-58603-942-4-75 (literal)
Alternative label
  • Lenci A.; Montemagni S.; Pirrelli V.; Giulia V. (2009)
    Ontology learning from Italian legal texts
    in Law, Ontologies and the Semantic Web - Channelling the Legal Information Flood, 2009
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Lenci A.; Montemagni S.; Pirrelli V.; Giulia V. (literal)
Pagina inizio
  • 75 (literal)
Pagina fine
  • 94 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#citta
  • Amsterdam (NL) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Law, Ontologies and the Semantic Web - Channelling the Legal Information Flood (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 188 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: Law, Ontologies and the Semantic Web - Channelling the Legal Information Flood. pp. 75 - 94. Joost Breuker, Pompeu Casanovas, Michel C.A. Klein, Enrico Francesconi (eds.). (Frontiers in Artificial Intelligence and Applications, vol. 188). Amsterdam (NL): New IOS Press Publication, 2009. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 20 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • The paper reports on the methodology and preliminary results of a case study in automatically extracting ontological knowledge from Italian legislative texts. We use a fully-implemented ontology learning system (T2K) that includes a battery of tools for Natural Language Processing (NLP), statistical text analysis and machine language learning. Tools are dynamically integrated to provide an incremental representation of the content of vast repositories of unstructured documents. Evaluated results, however preliminary, show the great potential of NLP-powered incremental systems like T2K for accurate large-scale semi-automatic extraction of legal ontologies. (literal)
Note
  • PuMa (literal)
  • Google Scholar (literal)
  • DBLP (literal)
  • CiteSeerX (literal)
  • ACM DL (literal)
  • PubZone (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Università di Pisa; ILC-CNR, Pisa (literal)
Titolo
  • Ontology learning from Italian legal texts (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#inCollana
  • Law, Ontologies and the Semantic Web - Channelling the Legal Information Flood (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-58603-942-4 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Joost Breuker; Pompeu Casanovas; Michel C.A. Klein; Enrico Francesconi (literal)
Abstract
  • The paper reports on the methodology and preliminary results of a case study in automatically extracting ontological knowledge from Italian legislative texts. We use a fully-implemented ontology learning system (T2K) that includes a battery of tools for Natural Language Processing (NLP), statistical text analysis and machine language learning. Tools are dynamically integrated to provide an incremental representation of the content of vast repositories of unstructured documents. Evaluated results, however preliminary, show the great potential of NLP-powered incremental systems like T2K for accurate large-scale semi-automatic extraction of legal ontologies. (literal)
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