FF-NSL: Feed-Forward Neural-Symbolic Learner

• URL: http://arxiv.org/abs/2106.13103v1
• Date: Thu, 24 Jun 2021 15:38:34 GMT
• Title: FF-NSL: Feed-Forward Neural-Symbolic Learner
• Authors: Daniel Cunnington, Mark Law, Alessandra Russo, Jorge Lobo
• Abstract summary: This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL) FF-NSL 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.
• Score: 70.978007919101
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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