Representation and reasoning of context-dependant knowledge in distributed fuzzy ontologies

  • Authors:

  • Yuncheng Jiang;Yong Tang;Ju Wang;Suqin Tang

  • Affiliations:

  • School of Computer Science, South China Normal University, Guangzhou 510631, PR China and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 1001 ...;School of Computer Science, South China Normal University, Guangzhou 510631, PR China;School of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, PR China;School of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, PR China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

Most of current knowledge based systems manage impressive amounts of information (especially distributed fuzzy information). In addition to widely pointed-out integration and maintenance difficulties, other common problem is overwhelming of users with much more information than the strictly necessary for fulfilling a task. This issue has been pointed out with the name of ''information overload''. Use of context knowledge has been envisioned as an appropriate solution to deal with this information overload matter. In this paper, we present a distributed fuzzy context-domain relevance (DFCDR) model for representation in fuzzy ontologies relevance relations between fuzzy context ontology and distributed fuzzy domain ontologies. In fact, the DFCDR model is a distributed fuzzy extension of the context-domain relevance (CDR) model.