An integrated approach for syllogistic reasoning and knowledge consistency level maintenance

We propose a new approach that integrates syllogistic reasoning and knowledge-base consistency level maintenance based on the framework of multi-objective decision-making. Syllogistic reasoning is formulated as a maximization process of the degree to which a priori inference rules (i.e., the prior knowledge) are compatible with given data (i.e., the empirical knowledge) while simultaneously using the rules to make inference from the data. Our method estimates the default values of missing membership values in the observed data, and, at the same time, the evaluation of the consistency degree of the prior knowledge is computed with the empirical knowledge. The unique feature of our method is the feedback capability of the empirical knowledge through the evaluation process in determining the most preferable default value. In contrast to the traditional inference methods that concentrates on the estimation part of the problem, leaving the evaluation part for some other “learning mechanism”, our method treats the dynamic interaction process between prior and empirical knowledge based on the multi-objective consistency maximization