NSL: Hybrid Interpretable Learning From Noisy Raw Data
This work addresses the challenge of learning interpretable rules from unstructured data for researchers and practitioners who need transparent and robust models.
This paper introduces NSL, a hybrid neural-symbolic learning framework that learns interpretable rules directly from noisy, unstructured data. NSL combines pre-trained neural networks for feature extraction with the FastLAS ILP system, demonstrating comparable or superior accuracy to neural network and random forest baselines on MNIST classification tasks while providing more general and interpretable rules.
Inductive Logic Programming (ILP) systems learn generalised, interpretable rules in a data-efficient manner utilising existing background knowledge. However, current ILP systems require training examples to be specified in a structured logical format. Neural networks learn from unstructured data, although their learned models may be difficult to interpret and are vulnerable to data perturbations at run-time. This paper introduces a hybrid neural-symbolic learning framework, called NSL, that learns interpretable rules from labelled unstructured data. NSL combines pre-trained neural networks for feature extraction with FastLAS, a state-of-the-art ILP system for rule learning under the answer set semantics. Features extracted by the neural components define the structured context of labelled examples and the confidence of the neural predictions determines the level of noise of the examples. Using the scoring function of FastLAS, NSL searches for short, interpretable rules that generalise over such noisy examples. We evaluate our framework on propositional and first-order classification tasks using the MNIST dataset as raw data. Specifically, we demonstrate that NSL is able to learn robust rules from perturbed MNIST data and achieve comparable or superior accuracy when compared to neural network and random forest baselines whilst being more general and interpretable.