Knowledge discovery in ontologies (Articolo in rivista)

Type
Label
  • Knowledge discovery in ontologies (Articolo in rivista) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.3233/IDA-2012-0536 (literal)
Alternative label
  • Furletti B., Turini F. (2012)
    Knowledge discovery in ontologies
    in Intelligent data analysis; IOS Press, Amsterdam (Paesi Bassi)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Furletti B., Turini F. (literal)
Pagina inizio
  • 513 (literal)
Pagina fine
  • 534 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 16 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 3 (literal)
Note
  • PuMa (literal)
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Department of Computer Science, University of Pisa, Pisa, Italy Department of Computer Science, University of Pisa, Pisa, Italy (literal)
Titolo
  • Knowledge discovery in ontologies (literal)
Abstract
  • Ontologies allow us to represent knowledge and data in implicit and explicit ways. Implicit knowledge can be derived by means of several deductive logic-based processes. This paper introduces a new way for extracting implicit knowledge from ontologies by means of a sort of link analysis of the T-box of the ontology integrated with a data mining step on the A-box. The implicit extracted knowledge has the form of In uence Rules\" i.e. rules structured as: if the property p1 of concept c1 has value v1, then the property p2 of concept c2 has value v2 with probability ?. The technique is completely general and applicable to whatever domain. The In uence Rules can be used to integrate existing knowledge or for supporting any other data mining process. A case study about an ontology describing intrusion detection is used to illustrate the result of the method. (literal)
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