A tuple-oriented algorithm for deduction in a fuzzy relational database

  • Authors:

  • Ignacio J. Blanco;Maria J. Martin-Bautista;Olga Pons;M. Amparo Vila

  • Affiliations:

  • Dept. Languages and Computer Science, University of Almería, Carretera de Sacramento S/N, La Cañada de San Urbano, 04120, Almería, SPAIN;Dept. Computer Science and Artificial Intelligence, University of Granada, Periodista Daniel Saucedo Aranda S/N, 18071, Granada, SPAIN;Dept. Computer Science and Artificial Intelligence, University of Granada, Periodista Daniel Saucedo Aranda S/N, 18071, Granada, SPAIN;Dept. Computer Science and Artificial Intelligence, University of Granada, Periodista Daniel Saucedo Aranda S/N, 18071, Granada, SPAIN

  • Venue:
  • International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - Intelligent information systems
  • Year:
  • 2003

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Abstract

In this paper, we define the concept of generalized rule for making classical deduction with imprecise data, stored both data and rules in a fuzzy relational database represented in the GEFRED model. We propose a way of measuring the imprecision related to the calculation of a fact based on the matching degree of the facts in the database and the facts calculated while expanding the rules. In order to achieve this, classical algorithms for deduction are not appropriated and we propose the modifications that have to be applied on a classical tuple-oriented algorithm in order to design a new algorithm for deducing from imprecise data with generalized rules.