Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases

The integration of heterogeneous databases into a unified querying framework remains a critical challenge, particularly in resource-constrained environments. This paper presents a novel Small Language Model (SLM)-driven system that synergizes advancements in lightweight Retrieval-Augmented Generation (RAG) and semantic-aware data structuring to enable efficient, accurate, and scalable query resolution across diverse data formats. By integrating semantic-aware heterogeneous graph indexing and topology-enhanced retrieval with SLM- powered structured data extraction, our system addresses the limitations of traditional methods in handling Multi-Entity Question Answering (Multi-Entity QA) and complex semantic queries. The introduction of semantic entropy as an unsupervised evaluation metric provides robust insights into model uncertainty. Together, these innovations establish a domain-agnostic, resource-efficient paradigm for executing complex queries across structured, semi-structured, and unstructured data sources, aiming at foundational advancement for next-generation intelligent database systems.