Authors:
Nouha Chaoued
1
;
Amel Borgi
2
and
Anne Laurent
3
Affiliations:
1
Université de Tunis El Manar, Faculté des Sciences de Tunis and Univ. Montpellier, Tunisia
;
2
Université de Tunis El Manar and Faculté des Sciences de Tunis, Tunisia
;
3
Univ. Montpellier, France
Keyword(s):
Imperfect Knowledge, Multi-valued Logic, Unbalanced Terms, Linguistic Modifiers.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Modeling human knowledge by machines should be as faithful as possible to reality. Therefore, it is imperative
to take account of inaccuracies and uncertainties in this knowledge. This problem has been dealt with
through different approaches. The most common approaches are fuzzy logic and multi-valued logic. These
two logics propose a linguistic term modeling. Generally, problems modeling qualitative aspect use linguistic
variables assessed in linguistic terms that are uniformly distributed on the scale. However, in many cases,
linguistic information needs to be defined by unbalanced term sets whose terms are not uniformly and/or not
symmetrically distributed. In the literature, it is shown that many researchers have dealt with these term sets
in the context of fuzzy logic. Thereby, in our work, we introduce a new approach to represent and treat such
term sets in the context of multi-valued logic. First, we propose an algorithm that allows representing terms
within an unbalanced set.
Then, we describe a second algorithm that permits the use of linguistic modifiers
within unbalanced multi-sets.
(More)