Stochastic Propositionalization for Efficient Multi-relational Learning

The efficiency of multi-relational data mining algorithms, addressing the problem of learning First Order Logic (FOL) theories, strongly depends on the search method used for exploring the hypotheses space and on the coverage test assessing the validity of the learned theory against the training examples. A way of tackling the complexity of this kind of learning systems is to use a propositional method that reformulates a multi-relational learning problem into an attribute-value one. We propose a population based algorithm that using a stochastic propositional method efficiently learns complete FOL definitions.