Neuro-LENS: A Neuro-Symbolic Framework Integrating Incomplete Background Knowledge and Deep Learning

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In this study, we propose Neuro-LENS, a neuro-symbolic evidence-based logic and symbolic reasoning framework that combines incomplete symbolic knowledge with neural learning to address ambiguity and improve the accuracy and interpretability of the results. We explore three strategies for integrating symbolic reasoning with deep learning and evaluate their effectiveness in practical settings: (i) applying the symbolic component to the neural output (neural-to-symbolic chaining); (ii) generating additional neural input features through symbolic rules (symbolic-to-neural chaining); (iii) creating an ensemble reasoning model (parallel neural-symbolic integration). The potential of the proposed Neuro-LENS framework is demonstrated on two real-world use cases: scene classification with abandoned object detection and prognostic health monitoring in vehicle failure prediction.