A Symbolic-based Model for Handling Negative Information

M. El-Sayed and D. Pacholczyk (France)

Keywords

: Linguistic Negation, Knowledge Management, Manyvalued logic, Multiset and Rough Set Theories.

Abstract

This paper treats the problem of negation processing and focuses on the linguistic negation rather than on the logical one. Our work is based on the main standard forms of lin guistic negation interpretations represented as "x is not A". The reference frame associated with a standard form con tains all its positive interpretations. The main goal of deal ing with negation is the selection of one (or several) pos itive interpretation(s) associated with a negative sentence from its reference frame. In this paper we don't research directly all affirmative interpretations of a negation, but we approximate its significance. We introduce two many valued operators, one is optimistic and the other is pes simistic. They are defined according to rough set theory. By using the new negation formulation, we propose sev eral generalizations of the Modus Ponens rules dealing with negative information. The new model is proposed within a symbolic many-valued predicate logic.

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