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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]
Copyright of Journal of Logic & Computation is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Learning typicality inclusions in a probabilistic description logic for concept combination and an application for recommending musical contents.
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  Data: <searchLink fieldCode="AR" term="%22Gliozzi%2C+Valentina%22">Gliozzi, Valentina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pozzato%2C+Gian+Luca%22">Pozzato, Gian Luca</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Valese%2C+Alberto%22">Valese, Alberto</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Logic+%26+Computation%22">Journal of Logic & Computation</searchLink>. Mar2026, Vol. 36 Issue 2, p1-26. 26p.
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  Data: <searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Description+logics%22">Description logics</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneity%22">Heterogeneity</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+representation+%28Information+theory%29%22">Knowledge representation (Information theory)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Logic & Computation is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1093/logcom/exaf066
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      – Code: eng
        Text: English
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        PageCount: 26
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      – SubjectFull: Classification
        Type: general
      – SubjectFull: Description logics
        Type: general
      – SubjectFull: Heterogeneity
        Type: general
      – SubjectFull: Knowledge representation (Information theory)
        Type: general
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      – TitleFull: Learning typicality inclusions in a probabilistic description logic for concept combination and an application for recommending musical contents.
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            NameFull: Gliozzi, Valentina
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            NameFull: Pozzato, Gian Luca
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            NameFull: Valese, Alberto
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              Text: Mar2026
              Type: published
              Y: 2026
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              Value: 36
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