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Boosting DL Concept Learners

  • Nicola Fanizzi
  • Giuseppe RizzoEmail author
  • Claudia d’Amato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11503)

Abstract

We present a method for boosting relational classifiers of individual resources in the context of the Web of Data. We show how weak classifiers induced by simple concept learners can be enhanced producing strong classification models from training datasets. Even more so the comprehensibility of the model is to some extent preserved as it can be regarded as a sort of concept in disjunctive form. We demonstrate the application of this approach to a weak learner that is easily derived from learners that search a space of hypotheses, requiring an adaptation of the underlying heuristics to take into account weighted training examples. An experimental evaluation on a variety of artificial learning problems and datasets shows that the proposed approach enhances the performance of the basic learners and is competitive, outperforming current concept learning systems.

References

  1. 1.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation and Applications, 2nd edn. Cambridge University Press, Cambridge (2007)zbMATHGoogle Scholar
  2. 2.
    Bühmann, L., Lehmann, J., Westphal, P.: DL-Learner - a framework for inductive learning on the Semantic Web. J. Web Sem. 39, 15–24 (2016)CrossRefGoogle Scholar
  3. 3.
    Cohen, W.W., Singer, Y.: A simple, fast, and effective rule learner. In: Hendler, J., Subramanian, D. (eds.) AAAI 1999/IAAI 1999, pp. 335–342. AAAI/MIT Press, Menlo Park (1999)Google Scholar
  4. 4.
    De Raedt, L.: Logical and Relational Learning. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-68856-3CrossRefzbMATHGoogle Scholar
  5. 5.
    Fanizzi, N.: Concept induction in Description Logics using information-theoretic heuristics. Int. J. Semantic Web Inf. Syst. 7(2), 23–44 (2011)CrossRefGoogle Scholar
  6. 6.
    Fanizzi, N., Rizzo, G., d’Amato, C., Esposito, F.: DLFoil: class expression learning revisited. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds.) EKAW 2018. LNCS (LNAI), vol. 11313, pp. 98–113. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03667-6_7CrossRefzbMATHGoogle Scholar
  7. 7.
    Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space. Morgan & Claypool, San Rafael (2011)Google Scholar
  8. 8.
    Hoche, S., Wrobel, S.: Relational learning using constrained confidence-rated boosting. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 51–64. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-44797-0_5CrossRefGoogle Scholar
  9. 9.
    Iannone, L., Palmisano, I., Fanizzi, N.: An algorithm based on counterfactuals for concept learning in the Semantic Web. Appl. Intell. 26(2), 139–159 (2007)CrossRefGoogle Scholar
  10. 10.
    Lehmann, J., Auer, S., Bühmann, L., Tramp, S.: Class expression learning for ontology engineering. J. Web Sem. 9, 71–81 (2011)CrossRefGoogle Scholar
  11. 11.
    Melo, A., Völker, J., Paulheim, H.: Type prediction in noisy RDF knowledge bases using hierarchical multilabel classification with graph and latent features. Int. J. Artif. Intell. Tools 26(2), 1–32 (2017)CrossRefGoogle Scholar
  12. 12.
    Quinlan, J.R.: Boosting first-order learning. In: Arikawa, S., Sharma, A.K. (eds.) ALT 1996. LNCS, vol. 1160, pp. 143–155. Springer, Heidelberg (1996).  https://doi.org/10.1007/3-540-61863-5_42CrossRefzbMATHGoogle Scholar
  13. 13.
    Rizzo, G., d’Amato, C., Fanizzi, N., Esposito, F.: Tree-based models for inductive classification on the web of data. J. Web Sem. 45, 1–22 (2017)CrossRefGoogle Scholar
  14. 14.
    Rizzo, G., Fanizzi, N., d’Amato, C., Esposito, F.: Approximate classification with web ontologies through evidential terminological trees and forests. Int. J. Approx. Reason. 92, 340–362 (2018)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Rowe, M., Stankovic, M., Alani, H.: Who will follow whom? Exploiting semantics for link prediction in attention-information networks. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 476–491. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35176-1_30CrossRefGoogle Scholar
  16. 16.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)CrossRefGoogle Scholar
  17. 17.
    Tran, A.C., Dietrich, J., Guesgen, H.W., Marsland, S.: Parallel symmetric class expression learning. J. Mach. Learn. Res. 18, 64:1–64:34 (2017)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Tran, T., Ha, Q., Hoang, T., Nguyen, L.A., Nguyen, H.S.: Bisimulation-based concept learning in description logics. Fundam. Inform. 133(2–3), 287–303 (2014)MathSciNetzbMATHGoogle Scholar

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Authors and Affiliations

  • Nicola Fanizzi
    • 1
  • Giuseppe Rizzo
    • 1
    Email author
  • Claudia d’Amato
    • 1
  1. 1.LACAM – Dipartimento di Informatica & CILAUniversità degli Studi di Bari Aldo MoroBariItaly

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