Learning Typicality Inclusions in a Probabilistic Description Logic for Concept Combination

Our paper introduces an innovative automated system designed to extract logical rules using the \(\textbf{T}^{\textsf {\tiny CL}}\) logic from diverse datasets, with a particular emphasis on tabular data. Our starting point is the CN2 algorithm. Typically employed for classification tasks, we have adapted this algorithm to suit our descriptive objectives. We consider well-known datasets (such as iris and zoo) to illustrate our approach. Furthermore, we extend this analysis to intricate datasets, notably the GTZAN musical dataset and the “Adult” dataset. These examples showcase the algorithm’s efficacy in generating descriptive rules across different data domains. We discuss the adaptability of the proposed approach across various data types, including images, sounds, and diverse heterogeneous structures.