FF-NSL: Feed-Forward Neural-Symbolic Learner
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Inductive Logic Programming (ILP) aims to learn generalised, interpretable hypotheses in a data-efficient manner. However, current ILP systems require training examples to be specified in a structured logical form. This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL), that integrates state-of-the-art ILP systems based on the Answer Set semantics, with neural networks, in order to learn interpretable hypotheses from labelled unstructured data. FF-NSL uses a pre-trained neural network to extract symbolic facts from unstructured data and an ILP system to learn a hypothesis that performs a downstream classification task. In order to evaluate the applicability of our approach to real-world applications, the framework is evaluated on tasks where distributional shifts are introduced to unstructured input data, for which pre-trained neural networks are likely to predict incorrectly and with high confidence. Experimental results show that FF-NSL outperforms baseline approaches such as a random forest and deep neural networks by learning more accurate and interpretable hypotheses with fewer examples.
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