Semantic-Aware Neural Symbolic Integration for Enhancing Reasoning Capabilities in Explainable Artificial Intelligence Systems
Keywords:
Neural-symbolic integration, semantic reasoning, hybrid intelligence, interpretable machine learning, symbolic logic, knowledge representationAbstract
The emergence of Explainable Artificial Intelligence (XAI) has elevated the demand for transparency in complex decision-making systems. This paper explores the integration of neural and symbolic reasoning models, emphasizing semantic-aware frameworks. By leveraging the representational strength of neural networks and the logic-based precision of symbolic systems, semantic-aware neural-symbolic integration (SNeSI) enhances the interpretability, consistency, and robustness of AI reasoning. We review foundational contributions, propose a conceptual model, and validate its reasoning performance on benchmark scenarios. Our findings underline the potential of SNeSI to bridge human cognitive expectations with machine intelligence.
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