Statistical Unfolded Logic Learning


Wang-Zhou Dai, Zhi-Hua Zhou ;
Asian Conference on Machine Learning, PMLR 45:349-361, 2016.


During the past decade, Statistical Relational Learning (SRL) and Probabilistic Inductive Logic Programming (PILP), owing to their strength in capturing structure information, have attracted much attention for learning relational models such as weighted logic rules. Typically, a generative model is assumed for the structured joint distribution, and the learning process is accomplished in an enormous relational space. In this paper, we propose a new framework, i.e., Statistical Unfolded Logic (SUL) learning. In contrast to learning rules in the relational space directly, SUL propositionalizes the structure information into an attribute-value data set, and thus, statistical discriminative learning which is much more efficient than generative relational learning can be executed. In addition to achieving better generalization performance, SUL is able to conduct predicate invention that is hard to be realized by traditional SRL and PILP approaches. Experiments on real tasks show that our proposed approach is superior to state-of-the-art weighted rules learning approaches.

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