A Hybrid Neuro-Symbolic Artificial Intelligence Architecture for Context-Aware Reasoning and Automated Knowledge Discovery

Abstract

This paper proposes a hybrid neuro-symbolic architecture integrating contextual representation learning, entity-relation extraction, a provenance-aware knowledge graph, differentiable rule scoring, and a constrained inference engine. The approach addresses limitations of purely statistical systems when decisions require explicit context, compositional reasoning, and auditable explanations. A staged processing pipeline combines transformer-derived embeddings with symbolic rules and uncertainty-calibrated evidence aggregation. The paper formalizes context-conditioned inference, contradiction penalties, and knowledge-discovery confidence, and specifies an evaluation protocol spanning predictive quality, rule consistency, robustness, and explanation fidelity. To demonstrate the expected format of evaluation, numerical tables and graphs use an explicitly labeled illustrative scenario rather than measurements from a completed experiment. The resulting research design offers a reproducible basis for future implementation and empirical testing in document intelligence, decision support, and scientific knowledge discovery.