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International Conference on Inductive Logic Programming

ILP 2012: Inductive Logic Programming pp 151–165Cite as

A Declarative Modeling Language for Concept Learning in Description Logics

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7842))

Abstract

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.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari “Aldo Moro”, Italy

    Francesca Alessandra Lisi

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  1. Francesca Alessandra Lisi
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Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy

    Fabrizio Riguzzi

  2. Department of Computer Science and Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Karlovo namesti 13, 12135, Prague 2, Republic Czech

    Filip Železný

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Lisi, F.A. (2013). A Declarative Modeling Language for Concept Learning in Description Logics. In: Riguzzi, F., Železný, F. (eds) Inductive Logic Programming. ILP 2012. Lecture Notes in Computer Science(), vol 7842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38812-5_11

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  • DOI: https://doi.org/10.1007/978-3-642-38812-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38811-8

  • Online ISBN: 978-3-642-38812-5

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