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 shiftsare 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.
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. To address this problem, this paper proposes 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 (NNs) , in order to learn interpretable hypotheses from labelled unstructured data. To demonstrate the generality and robustness of FF-NSL, we use two datasets subject to distributional shifts , for which pre-trained NNs may give incorrect predictions with high confidence. Experimental results show that FF-NSL outperforms tree-based and neural-based approaches by learning more accurate and interpretable hypotheses with fewer examples.
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This paper introduces
To address this problem, this paper proposes
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,
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neural networks
Neural Networks (NNs)
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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 shiftsare introduced to unstructured input data
To demonstrate the generality and robustness of FF-NSL, we use two datasets subject to distributional shifts
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neural networks are likely to predict incorrectly and
NNs may give incorrect predictions
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baseline approaches such as a random forest and deep neural networks
tree-based and neural-based approaches