Dynamic User-Defined Similarity Searching in Semi-Structured Text Retrieval (Rapporti tecnici, manuali, carte geologiche e tematiche e prodotti multimediali)

Type
Label
  • Dynamic User-Defined Similarity Searching in Semi-Structured Text Retrieval (Rapporti tecnici, manuali, carte geologiche e tematiche e prodotti multimediali) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Alternative label
  • Geraci F., Pellegrini M. (2007)
    Dynamic User-Defined Similarity Searching in Semi-Structured Text Retrieval
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Geraci F., Pellegrini M. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • http://puma.isti.cnr.it//linkdoc.php?idauth=18&idcol=17&icode=2007-TR-007&authority=cnr.iit&collection=cnr.iit&langver=it (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • rapporti tecnici IIT 2007-TR-007 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • Modern text retrieval systems often provide a similarity search utility, that allows the user to find efficiently a fixed number k of documents in the data set that are most similar to a given query (here a query is either a simple sequence of keywords or the identifier of a full document found in previous searches that is considered of interest). We consider the case of a textual database made of semi-structured documents. For example, in a corpus of bibliographic records any record may be structured into three fields: title, authors and abstract, where each field is an unstructured free text. Each field, in turns, is modelled with a specific vector space. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#supporto
  • Altro (literal)
Titolo
  • Dynamic User-Defined Similarity Searching in Semi-Structured Text Retrieval (literal)
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