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Learning typicality inclusions in a probabilistic description logic for concept combination and an application for recommending musical contents.

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Title: Learning typicality inclusions in a probabilistic description logic for concept combination and an application for recommending musical contents.
Authors: Gliozzi, Valentina1 (AUTHOR), Pozzato, Gian Luca1 (AUTHOR), Valese, Alberto1 (AUTHOR)
Source: Journal of Logic & Computation. Mar2026, Vol. 36 Issue 2, p1-26. 26p.
Subjects: Classification, Description logics, Heterogeneity, Knowledge representation (Information theory)
Abstract: Our paper introduces an innovative automated system designed to extract logical rules using the |$\textbf{T}^{\mathsf{\tiny CL}}$| logic from various 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 a complex dataset, notably the GTZAN musical dataset. We have then tested our system by reclassifying the songs available in the GTZAN database with respect to the newly generated musical genres, obtaining encouraging results. This example showcases 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 various heterogeneous structures. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Our paper introduces an innovative automated system designed to extract logical rules using the |$\textbf{T}^{\mathsf{\tiny CL}}$| logic from various 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 a complex dataset, notably the GTZAN musical dataset. We have then tested our system by reclassifying the songs available in the GTZAN database with respect to the newly generated musical genres, obtaining encouraging results. This example showcases 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 various heterogeneous structures. [ABSTRACT FROM AUTHOR]
ISSN:0955792X
DOI:10.1093/logcom/exaf066