SkILL - a Stochastic Inductive Logic Learner

Joana Côrte-Real, Theofrastos Mantadelis, Inês Dutra, Ricardo Rocha and Elizabeth Burnside

December 2015

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Abstract

Probabilistic Inductive Logic Programming (PILP) is a relatively unexplored area of Statistical Relational Learning which extends classic Inductive Logic Programming (ILP). Within this scope, we introduce SkILL, a Stochastic Inductive Logic Learner, which takes probabilistic annotated data and produces First Order Logic (FOL) theories. Data in several domains such as medicine and bioinformatics have an inherent degree of uncertainty, and because SkILL can handle this type of data, the models produced for these areas are closer to reality. SkILL can then use probabilistic data to extract non-trivial knowledge from databases, and also addresses efficiency issues by introducing an efficient search strategy for finding hypotheses in PILP environments. SkILL’s capabilities are demonstrated using a real world medical dataset in the breast cancer domain.

Bibtex

@InProceedings{corte-real-icmla15,
  author =    {J. Côrte-Real and T. Mantadelis and I. Dutra and R. Rocha and E. Burnside},
  title =     {{SkILL - a Stochastic Inductive Logic Learner}},
  booktitle = {Proceedings of the 14th International Conference on Machine Learning and Applications (ICMLA 2015)},
  pages =     {555--558},
  editor =    {Arif Wani},
  month =     {December},
  year =      {2015},
  address =   {Miami, Florida, USA},
}

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