A framework for linguistic modelling
Artificial Intelligence
An introduction to bipolar representations of information and preference
International Journal of Intelligent Systems
Uncertainty modelling for vague concepts: A prototype theory approach
Artificial Intelligence
Fuzzy Sets and Systems
Granular knowledge representation and inference using labels and label expressions
IEEE Transactions on Fuzzy Systems - Special section on computing with words
A prototype-based rule inference system incorporating linear functions
Fuzzy Sets and Systems
A bipolar model of vague concepts based on random set and prototype theory
International Journal of Approximate Reasoning
On truth-gaps, bipolar belief and the assertability of vague propositions
Artificial Intelligence
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An interval model for linguistic labels is proposed by introducing bipolar semantic cells for concept representation. According to this model, the degree to which each element is a positive case of a given linguistic expression is an interval value. Fundamental to our approach is that there is an uncertain border area associated with linguistic labels. This is modeled by assuming that there are two uncertain boundaries for each linguistic label, resulting in a bipolar semantic cell for concept representation. The calculus of lower and upper neighborhood functions of linguistic expressions is developed and investigated. This then provides a framework for modelliong the vague concepts in uncertain reasoning.