A Neuro-Symbolic Approach to Symbol Grounding for ALC-Ontologies

Neuro-symbolic computing aims to integrate neural learning with symbolic reasoning to address the fundamental challenge of symbol grounding. While neural networks excel at pattern recognition, they struggle to maintain logical consistency. Conversely, symbolic systems provide formal reasoning capabilities but lack mechanisms for handling perceptual uncertainty. This paper introduces EmALC, a novel neuro-symbolic framework that bridges neural perception with symbolic logic through differentiable fuzzy semantics. Our approach addresses a key limitation of existing methods: while previous neuro-symbolic approaches like Logic Tensor Networks employ first-order fuzzy logic, where key reasoning problems are undecidable, EmALC ensures decidable reasoning by leveraging a fuzzy variant of ALC -- a decidable fragment of first-order logic. Unlike previous approaches that often compromise logical soundness for learning capability, EmALC maintains provable semantic consistency through a hierarchical loss function while mitigating reasoning shortcuts via rule-based revision strategies. Experimental evaluation demonstrates EmALC's effectiveness: on ontology revision tasks, it achieves 100% success rate in correcting masked groundings while preserving semantic integrity; on semantic image interpretation tasks, it improves object classification F1-scores by up to 5.56% through ontology-guided knowledge revision.