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arXiv · 1506.00893

SkILL - a Stochastic Inductive Logic Learner

Joana Côrte-Real·Theofrastos Mantadelis·Inês Dutra·Ricardo Rocha
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Abstract

Probabilistic Inductive Logic Programming (PILP) is a rel- atively unexplored area of Statistical Relational Learning which extends classic Inductive Logic Programming (ILP). This work introduces SkILL, a Stochastic Inductive Logic Learner, which takes probabilistic annotated data and produces First Order Logic theories. Data in several domains such as medicine and bioinformatics have an inherent degree of uncer- tainty, that can be used to produce models closer to reality. SkILL can not only use this type of probabilistic data to extract non-trivial knowl- edge from databases, but it also addresses efficiency issues by introducing a novel, efficient and effective search strategy to guide the search in PILP environments. The capabilities of SkILL are demonstrated in three dif- ferent datasets: (i) a synthetic toy example used to validate the system, (ii) a probabilistic adaptation of a well-known biological metabolism ap- plication, and (iii) a real world medical dataset in the breast cancer domain. Results show that SkILL can perform as well as a deterministic ILP learner, while also being able to incorporate probabilistic knowledge that would otherwise not be considered.

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Joana Côrte-Real, Theofrastos Mantadelis, Inês Dutra, Ricardo Rocha. 2015-06-02. SkILL - a Stochastic Inductive Logic Learner. https://arxiv.org/abs/1506.00893

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