FUZRUF-onto: a Methodology to Develop Fuzzy Rough Ontologies

Abstract

Abstract Nowadays, semantic web technologies play a crucial role in knowledge representation paradigm. With the raise of imprecise and vague knowledge, there is an upsurge demand in applying a concrete well-established procedure to represent such knowledge. Ontologies, particularly fuzzy ontologies are increasingly applied in application scenarios in which handling of vague knowledge is significant. However, such fuzzy ontologies utilize fuzzy set theory to provide quantitative methods to manage vagueness. In various cases of real-life scenarios, people need to express their everyday requirements using linguistic adverbs such as very, exactly, mostly, possibly, etc. The aim is to show how fuzzy properties can be complemented by Rough Set methods to capture another type of imprecision caused by approximation spaces. Rough sets theory offers a qualitative approach to model such vagueness via describing fuzzy properties at multiple levels of granularity using approximation sets. Using rough-set theory, each fuzzy concept is represented by two approximations. The lower approximation PL(C) consists of a set of fuzzy properties that are definitely observable in the concept. The upper approximation PU(C) on the other hand contains fuzzy properties that are possibly associated with the concept but may not be observed. This paper introduces a methodology named FUZRUF-onto methodology, which is a formal guidance on how to build fuzzy rough ontologies from scratch using extensive research in the area of fuzzy rough combination. Fuzzy set and rough set theories are applied to capture the inherently fuzzy relationships among concepts expressed by natural languages. The methodology provides a very good guideline for formally constructing fuzzy rough ontologies in terms of completeness, correctness, consistency, understandability, and conciseness. To explain how the FUZRUF-onto works, and demonstrate its usefulness, a practical step by step example is provided.