• DocumentCode
    2970723
  • Title

    Ontology-Based Business Plan Classification

  • Author

    Baglioni, Miriam ; Bellandi, Andrea ; Furletti, Barbara ; Spinsanti, Laura ; Turini, Franco

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Pisa, Pisa
  • fYear
    2008
  • fDate
    15-19 Sept. 2008
  • Firstpage
    365
  • Lastpage
    371
  • Abstract
    The problem of providing small and medium enterprises (SMEs) with good self-assessment tools is becoming more and more urgent every day, not only because of increasing market competition, but also because of new rules for credit granting, as for example the ones referred to as Basel II.One of the critical issues in designing supporting tools is the quality of the knowledge embedded in them. We maintain that a better quality of decisions can be obtained by exploiting not only quantitative information but also qualitative information and expert knowledge. Here we present a system able to classify the quality of innovation plans of SMEs by exploiting both quantitative and qualitative knowledge embedded in ontology. The ontological approach allows representing qualitative knowledge in a very natural way and, as a consequence, we are able to elicit it by means that are natural for SME officers, as for example questionnaires.
  • Keywords
    innovation management; ontologies (artificial intelligence); small-to-medium enterprises; Basel II; business plan classification; credit granting; innovation plan quality; ontology; self-assessment tools; small and medium enterprises; Buildings; Classification tree analysis; Computer science; Distributed computing; Information analysis; Machine learning; Merging; Ontologies; Proposals; Technological innovation; Business documents Classification; Ontology; SMEs self-assessment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Enterprise Distributed Object Computing Conference, 2008. EDOC '08. 12th International IEEE
  • Conference_Location
    Munich
  • ISSN
    1541-7719
  • Print_ISBN
    978-0-7695-3373-5
  • Type

    conf

  • DOI
    10.1109/EDOC.2008.30
  • Filename
    4634789