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Learning Probabilistic Description Logics

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Uncertainty Reasoning for the Semantic Web III (URSW 2012, URSW 2011, URSW 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8816))

Abstract

We consider the problem of learning both the structure and the parameters of Probabilistic Description Logics under the DISPONTE semantics. DISPONTE is based on the distribution semantics for Probabilistic Logic Programming and assigns a probability to assertional and terminological axioms. The system EDGE, given a DISPONTE knowledge base (KB) and sets of positive and negative examples in the form of concept assertions, returns the value of the probabilities associated with axioms. We present the system LEAP that learns both the structure and the parameters of DISPONTE KBs explotiting EDGE. LEAP is based on the system CELOE for ontology engineering and exploits its search strategy in the space of possible axioms. LEAP uses the axioms returned by CELOE to build a KB so that the likelihood of the examples is maximized. We present experiments showing the potential of EDGE and LEAP.

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Notes

  1. 1.

    http://dl-learner.org/Projects/DLLearner

  2. 2.

    http://education.data.gov.uk/

  3. 3.

    http://dbpedia.org/About

  4. 4.

    http://dl-learner.org/wiki/Carcinogenesis

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Authors and Affiliations

  1. Dipartimento di Matematica e Informatica, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy

    Fabrizio Riguzzi

  2. Dipartimento di Ingegneria, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy

    Elena Bellodi, Evelina Lamma, Riccardo Zese & Giuseppe Cota

Authors
  1. Fabrizio Riguzzi
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  2. Elena Bellodi
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  3. Evelina Lamma
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  4. Riccardo Zese
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  5. Giuseppe Cota
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Corresponding author

Correspondence to Fabrizio Riguzzi .

Editor information

Editors and Affiliations

  1. University of Zaragoza, Zaragoza, Spain

    Fernando Bobillo

  2. Universidade de Brasília, Brasília, Brazil

    Rommel N. Carvalho

  3. George Mason University, Fairfax, Virginia, USA

    Paulo C.G. Costa

  4. Università degli Studi di Bari, Bari, Italy

    Claudia d'Amato

  5. Università degli Studi di Bari, Bari, Italy

    Nicola Fanizzi

  6. George Mason University, Fairfax, Virginia, USA

    Kathryn B. Laskey

  7. MITRE Corporation, McLean, Virginia, USA

    Kenneth J. Laskey

  8. University of Oxford, Oxford, United Kingdom

    Thomas Lukasiewicz

  9. National University of Ireland, Galway, Ireland

    Matthias Nickles

  10. Goldman Sachs, Washington, District of Columbia, USA

    Michael Pool

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Riguzzi, F., Bellodi, E., Lamma, E., Zese, R., Cota, G. (2014). Learning Probabilistic Description Logics. In: Bobillo, F., et al. Uncertainty Reasoning for the Semantic Web III. URSW URSW URSW 2012 2011 2013. Lecture Notes in Computer Science(), vol 8816. Springer, Cham. https://doi.org/10.1007/978-3-319-13413-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-13413-0_4

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  • Online ISBN: 978-3-319-13413-0

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