Probabilistic Inductive Logic Programming on the Web

  • Fabrizio Riguzzi
  • Riccardo Zese
  • Giuseppe Cota
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10180)


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.



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


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabrizio Riguzzi
    • 1
  • Riccardo Zese
    • 2
  • Giuseppe Cota
    • 2
  1. 1.Dipartimento di Matematica e InformaticaUniversity of FerraraFerraraItaly
  2. 2.Dipartimento di IngegneriaUniversity of FerraraFerraraItaly

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