Improving Neural-based Classification with Logical Background Knowledge

arXiv:2402.13019v1 Announce Type: cross
Abstract: Neurosymbolic AI is a growing field of research aiming to combine neural networks learning capabilities with the reasoning abilities of symbolic systems. This hybridization can take many shapes. In this paper, we propose a new formalism for supervised multi-label classification with propositional background knowledge. We introduce a new neurosymbolic technique called semantic conditioning at inference, which only constrains the system during inference while leaving the training unaffected. We discuss its theoritical and practical advantages over …

abstract arxiv capabilities classification cs.ai cs.lg cs.sc knowledge networks neural networks paper reasoning research systems type