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

arXiv: https://arxiv.org/abs/2504.05634 · HTML: https://mlsanity.com/paper/2504.05634/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2025.

## Built on by (most cited first, 5 of 5)

- [LightKGG: Simple and Efficient Knowledge Graph Generation from Textual Data](https://mlsanity.com/paper/2510.23341/) (2025)
- [DocSage: An Information Structuring Agent for Multi-Doc Multi-Entity Question Answering](https://mlsanity.com/paper/2603.11798/) (2026)
- [Monte Carlo Tree Search for Table-to-Multimodal Report Generation](https://mlsanity.com/paper/2608.04071/) (2026)
- [EidosDoc: Implicit Structure Encoding for Cost-Effective Semi-Structured Document QA](https://mlsanity.com/paper/2609.27784/) (2026)
- [Structured Composition of Verifiable Atomic Insights for Table-to-Report Generation](https://mlsanity.com/paper/2610.03525/) (2026)

## Abstract (arXiv)

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 MiniRAG's 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. Experimental results demonstrate superior performance in accuracy and efficiency, while the introduction of semantic entropy as an unsupervised evaluation metric provides robust insights into model uncertainty. This work pioneers a cost-effective, domain-agnostic solution for next-generation database systems.