ADAPTIVE KNOWLEDGE-AUGMENTED DATABASE SYSTEMS FOR REAL-TIME QUERY OPTIMIZATION AND SEMANTIC DATA INTEGRATION IN HETEROGENEOUS ENVIRONMENTS: A HYBRID NEURO-SYMBOLIC FRAMEWORK COMBINING GRAPH-BASED REASONING, NATURAL LANGUAGE INTERFACES, AND AUTO ML-DR

In the era of data deluge and heterogeneous information systems, traditional database architectures face critical challenges in handling complex semantic queries and real-time optimization. This paper proposes an adaptive knowledge-augmented database framework that integrates neuro-symbolic AI, graph-based reasoning, and AutoML-driven indexing to enable semantic data integration and efficient query processing. By leveraging natural language interfaces and domain knowledge graphs, the system supports dynamic adaptation to query intents across disparate datasets. Experimental evaluation demonstrates improved response time, query accuracy, and semantic richness compared to conventional systems.