Probabilistic Inductive Logic Programming on the Web

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Notes

Acknowledgement

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

References

  1. 1.
    Bellodi, E., Riguzzi, F.: Expectation maximization over binary decision diagrams for probabilistic logic programs. Intell. Data Anal. 17(2), 343–363 (2013)Google Scholar
  2. 2.
    Bellodi, E., Riguzzi, F.: Structure learning of probabilistic logic programs by searching the clause space. Theor. Pract. Log. Program. 15(2), 169–212 (2015)CrossRefGoogle Scholar
  3. 3.
    Raedt, L., Kersting, K.: Probabilistic inductive logic programming. In: Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.) Probabilistic Inductive Logic Programming. LNCS, vol. 4911, pp. 1–27. Springer, Heidelberg (2008). doi:10.1007/978-3-540-78652-8_1 CrossRefGoogle Scholar
  4. 4.
    Fierens, D., den Broeck, G.V., Renkens, J., Shterionov, D.S., Gutmann, B., Thon, I., Janssens, G., De Raedt, L.: Inference and learning in probabilistic logic programs using weighted boolean formulas. Theor. Pract. Log. Program. 15(3), 358–401 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Goodman, N.D., Tenenbaum, J.B.: Probabilistic Models of Cognition. http://probmods.org
  6. 6.
    Riguzzi, F., Bellodi, E., Lamma, E., Zese, R., Cota, G.: Probabilistic logic programming on the web. Softw. Pract. Exp. 46(10), 1381–1396 (2016)CrossRefMATHGoogle Scholar
  7. 7.
    Riguzzi, F., Cota, G.: Probabilistic logic programming tutorial. Assoc. Log. Program. Newsl. 29(1) (2016). http://www.cs.nmsu.edu/ALP/2016/03/probabilistic-logic-programming-tutorial/
  8. 8.
    Sato, T.: A statistical learning method for logic programs with distribution semantics. In: Sterling, L. (ed.) ICLP-95, pp. 715–729. MIT Press, Cambridge (1995)Google Scholar

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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