Transforming fuzzy graphs into linguistic variables

Abstract

Fuzzy graphs (FG) are capable of showing dependencies and relationships between each other to a certain degree. Often, these relationships are described by numbers, which impedes interpretability for humans because they communicate using natural language. This paper seeks to turn the mathematical output of an FG into natural language sentences by applying Restriction-Centered Theory (RCT) to enhance the possibilities of knowledge transfer for humans via an FG. The proposed framework connects FGs and the RCT to produce not only verbalized dependencies but also statements about the dependencies of FGs. As a proof of concept, a use case is introduced, where Swiss Airline's connecting passenger flows are analyzed. The statements of the framework's output are verified by an expert at the company that owns the data.