Positive-Unlabeled Learning with Knowledge-Structured Data

Positive-Unlabeled (PU) learning focuses on scenarios where only positive and unlabeled examples are available and has become a crucial area in machine learning. However, its application to data enriched with structured or symbolic knowledge remains unstudied. Existing PU learning methods often rely on the assumption of irreducibility between negative and positive distributions, but this assumption has not been studied in settings where the data is governed by logical rules or relational constraints. In this work, we formalize PU learning for knowledge-driven data and introduce a novel approach that leverages a neuro-symbolic framework to address the PU learning problem in such data settings. We further propose NeuralAdjust, a method that uses neural networks to dynamically reweight logical rules. This helps mitigate violations of the irreducibility condition induced by relational conflicts. Our experimental results demonstrate robust performance across diverse datasets and knowledge structures, surpassing previous state-of-the-art graph-based methods for PU learning. https://github.com/haiyenvu96/NeuralAdjust-for-NeuPSL-PU .