Ontologically Enriched Rough Set Based Reasoning in Medical Databases with Linguistic Data

Originally, in rough set theory (RST) proposed by Z. Pawlak, approximation of sets is defined on the basis of an indiscernibility relation between objects in some universe of discourse. The problems appear if attribute values describing objects are symbolical (e.g., linguistic terms). Such a situation is natural in human cognition and description of the real world (e.g. in case of medical applications, where diseases are described in natural language terms). We can perfect rough set theory in this area by incorporating ontologies enabling us to add some new, valuable knowledge, which can be used in data analysis, rule generation, reasoning, etc. In the paper, we propose to use ontological graphs in determining approximations of sets as well as we show how ontological graphs change the look at them in case of linguistic medical data.