• DocumentCode
    478587
  • Title

    Human-Readable and Machine-Readable Knowledge Bases Using Specialized Word Processors

  • Author

    Molina, Martin ; Blasco, Gemma

  • Author_Institution
    Dept. de Intel. Artificial, Univ. Politec. de Madrid, Madrid
  • Volume
    1
  • fYear
    2008
  • fDate
    3-5 Nov. 2008
  • Firstpage
    103
  • Lastpage
    110
  • Abstract
    The maintenance of knowledge bases is one of the crucial activities in the life cycle of knowledge systems. This paper describes an innovative approach to write complex and large knowledge bases using specialized word processors. According to this, a knowledge model is represented as a conventional document that is written following the standard operations of word processors. Following this approach, domain experts that are not familiar with computer languages could easier read and write complex knowledge models. In addition to that, the processor is able of interpreting the content of the document to automatically perform tasks of the knowledge model. The paper describes the basic characteristics of the document and its specialized word processor and presents our experience following this approach for a knowledge system in the domain of hydrology.
  • Keywords
    knowledge based systems; knowledge representation; word processing; computer languages; domain experts; human-readable knowledge bases; innovative approach; knowledge systems; machine-readable knowledge bases; specialized word processors; Artificial intelligence; Computer languages; Hydrology; Knowledge based systems; Knowledge engineering; Knowledge representation; Predictive models; Proposals; Software tools; Writing; document-based representation; knowledge based systems; knowledge engineering; maintenance of knowledge bases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2008. ICTAI '08. 20th IEEE International Conference on
  • Conference_Location
    Dayton, OH
  • ISSN
    1082-3409
  • Print_ISBN
    978-0-7695-3440-4
  • Type

    conf

  • DOI
    10.1109/ICTAI.2008.33
  • Filename
    4669677