A Declarative Modeling Language for Concept Learning in Description Logics

  • Francesca Alessandra Lisi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7842)


Learning in Description Logics (DLs) has been paid increasing attention over the last decade. Several and diverse approaches have been proposed which however share the common feature of extending and adapting previous work in Concept Learning to the novel representation framework of DLs. In this paper we present a declarative modeling language for Concept Learning in DLs which relies on recent results in the fields of Knowledge Representation and Machine Learning. Based on second-order DLs, it allows for modeling Concept Learning problems as constructive DL reasoning tasks where the construction of the solution to the problem may be subject to optimality criteria.


Description Logic Constraint Programming Concept Description Concept Variable Concept Expression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francesca Alessandra Lisi
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari “Aldo Moro”Italy

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