Evidence-based model for 2-uncertain rules and inexact reasoning

In empirical sciences, among others - in medicine, domain data - stored in different repositories - are the most important source of domain information. There is a great number of methods, including semantic data integration, that enable to acquire domain knowledge from such data and express it in a convenient form. In the paper we propose a model for rules with uncertainty (2-uncertain rules) that can be obtained from somewhat heterogeneous data, written in a common format of tuples. The rules are uncertain implications, with complex premises and single conclusions, and two specific reliability factors. In addition, we propose functions for propagating uncertainty through reasoning chains in Rule-Based Systems (RBSs) with such rules in their knowledge base