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European Knowledge Acquisition Workshop

EKAW 2016: Knowledge Engineering and Knowledge Management pp 172–175Cite as

Probabilistic Inductive Logic Programming on the Web

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10180))

Abstract

Probabilistic Inductive Logic Programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning algorithms for PLP, SLIPCOVER. Moreover, an online version was recently developed, cplint on SWISH, that allows users to experiment with the system using just a web browser. In this demo we illustrate cplint on SWISH concentrating on structure learning with SLIPCOVER. cplint on SWISH also includes many examples and a step-by-step tutorial.

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Notes

  1. 1.

    https://dtai.cs.kuleuven.be/problog/.

  2. 2.

    https://ace.c9.io/.

  3. 3.

    http://cplint.lamping.unife.it/example/learning/learning_examples.swinb.

  4. 4.

    https://dtai.cs.kuleuven.be/ACE/.

  5. 5.

    http://cplint.lamping.unife.it/example/learning/mach.pl.

  6. 6.

    http://cplint.lamping.unife.it/example/learning/exauc.pl.

References

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Acknowledgement

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

Author information

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

    Riccardo Zese & Giuseppe Cota

Authors
  1. Fabrizio Riguzzi
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  2. Riccardo Zese
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  3. Giuseppe Cota
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Corresponding author

Correspondence to Fabrizio Riguzzi .

Editor information

Editors and Affiliations

  1. University of Bologna , Bologna, Italy

    Paolo Ciancarini

  2. University of Bologna , Bologna, Italy

    Francesco Poggi

  3. Stanford University , Palo Alto, California, USA

    Matthew Horridge

  4. Lancaster University , Lancaster, United Kingdom

    Jun Zhao

  5. Garvan Institute of Medical Research , Darlinghurst, New South Wales, Australia

    Tudor Groza

  6. Universidad Politecnica de Madrid , Boadilla del Monte, Spain

    Mari Carmen Suarez-Figueroa

  7. The Open University , Milton Keynes, United Kingdom

    Mathieu d'Aquin

  8. STLab ISTC-CNR , Rome, Italy

    Valentina Presutti

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Riguzzi, F., Zese, R., Cota, G. (2017). Probabilistic Inductive Logic Programming on the Web. In: Ciancarini, P., et al. Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10180. Springer, Cham. https://doi.org/10.1007/978-3-319-58694-6_25

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  • DOI: https://doi.org/10.1007/978-3-319-58694-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58693-9

  • Online ISBN: 978-3-319-58694-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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