On Applying Tabling to Inductive Logic Programming

Ricardo Rocha, Nuno Fonseca and VĂ­tor Santos Costa

October 2005


Inductive Logic Programming (ILP) is an established sub-field of Machine Learning. Nevertheless, it is recognized that efficiency and scalability is a major obstacle to an increased usage of ILP systems in complex applications with large hypotheses spaces. In this work, we focus on improving the efficiency and scalability of ILP systems by exploring tabling mechanisms available in the underlying Logic Programming systems. Tabling is an implementation technique that improves the declarativeness and performance of Prolog systems by reusing answers to subgoals. To validate our approach, we ran the April ILP system in the YapTab Prolog tabling system using two well-known datasets. The results obtained show quite impressive gains without changing the accuracy and quality of the theories generated.


  author =    {R. Rocha and N. Fonseca and V. Santos Costa},
  title =     {{On Applying Tabling to Inductive Logic Programming}},
  booktitle = {Proceedings of the 16th European Conference on Machine Learning (ECML 2005)},
  pages =     {707--714},
  number =    {3720},
  series =    {LNAI},
  publisher = {Springer},
  editor =    {J. Gama and R. Camacho and P. Brazdil and A. Jorge and L. Torgo},
  month =     {October},
  year =      {2005},
  address =   {Porto, Portugal},

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