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A Modular Inference System for Probabilistic Description Logics

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Scalable Uncertainty Management (SUM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11142))

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Abstract

While many systems exist for reasoning with Description Logics knowledge bases, very few of them are able to cope with uncertainty. BUNDLE is a reasoning system, exploiting an underlying non-probabilistic reasoner (Pellet), able to perform inference w.r.t. Probabilistic Description Logics. In this paper, we report on a new modular version of BUNDLE that can use other OWL (non-probabilistic) reasoners and various approaches to perform probabilistic inference. BUNDLE can now be used as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. Due to the introduced modularity, BUNDLE performance now strongly depends on the method and OWL reasoner chosen to obtain the set of justifications. We provide an evaluation on several datasets as the inference settings vary.

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Notes

  1. 1.

    https://github.com/matthewhorridge/owlexplanation.

  2. 2.

    http://jfact.sourceforge.net/.

  3. 3.

    http://owlcs.github.io/owlapi/.

  4. 4.

    With groupId it.unife.endif.ml, artifactId bundle and version 3.0.0.

  5. 5.

    http://www.hpc.cineca.it/hardware/marconi.

  6. 6.

    http://dbpedia.org/.

  7. 7.

    http://www.biopax.org/.

  8. 8.

    http://www.vicodi.org/.

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Acknowledgement

This work was supported by the “GNCS-INdAM”.

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Authors and Affiliations

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

    Giuseppe Cota, Riccardo Zese & Evelina Lamma

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

    Fabrizio Riguzzi & Elena Bellodi

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

Correspondence to Giuseppe Cota .

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Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  3. Sapienza University of Rome, Rome, Italy

    Barbara Vantaggi

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Cota, G., Riguzzi, F., Zese, R., Bellodi, E., Lamma, E. (2018). A Modular Inference System for Probabilistic Description Logics. In: Ciucci, D., Pasi, G., Vantaggi, B. (eds) Scalable Uncertainty Management. SUM 2018. Lecture Notes in Computer Science(), vol 11142. Springer, Cham. https://doi.org/10.1007/978-3-030-00461-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-00461-3_6

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