Εργαστήριο Γνώσης και Αβεβαιότητας

Knowledge and Uncertainty Research Laboratory

Utilizing Imprecise Knowledge in Ontology-based CBR Systems by Means of Fuzzy Algebra

[journal]


Full reference

P. Alexopoulos, M. Wallace, K. Kafentzis, and D. Askounis, Utilizing Imprecise Knowledge in Ontology-based CBR Systems by Means of Fuzzy Algebra, International Journal of Fuzzy Systems 12(1), pp 1-14, 2010


Abstract

Case Based Reasoning (CBR) is a problem-solving paradigm that uses knowledge of relevant past experiences (cases) to interpret or solve new problems. An evolvement to this paradigm is ontology-based CBR, an approach that combines, in the form of formal ontologies, case speci¯c knowledge with domain one in order to improve the e®ectiveness of the CBR process. This e®ectiveness is further improved if ontology- based CBR systems were able to utilize knowledge that is vague or im- precise; to that end, we present in this paper a novel CBR approach that manages and utilizes imprecise knowledge through the integration of Fuzzy Algebra in the ontology-based CBR paradigm. The approach has been applied in real life and constitutes the core of a portal that provides the public with intelligent access to knowledge assets.

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3 known citations

  1. Kotis K., P. Alexopoulos, A. Papasalouros (2010), Towards a Framework for Trusting the Automated Learning of Social ontologies, Knowledge Science, Engineering and Management (KSEM) 2010, Belfast, Northern Ireland, September 1-3
  2. I. Zisis, Developement of a vague knowledge management system, Thesis, National Technical University of Athens, 2011
  3. Devadoss, Nilavu, and Sivakumar Ramakrishnan, Development of Fuzzy Rough Features in Ontology Knowledge Representation, The Journal of Technology Volume 6 Oct. 2014, Pages 265-284