Routing End User Queries to Enterprise Databases
Saikrishna Sudarshan, Tanay Kulkarni, Manasi Patwardhan, Lovekesh Vig, Ashwin Srinivasan, Tanmay Tulsidas Verlekar

TL;DR
This paper presents a reasoning-based reranking approach for routing natural language queries to the correct enterprise databases, addressing challenges posed by large, overlapping schemas and ambiguous queries, and demonstrating superior performance over baseline methods.
Contribution
It introduces a modular, reasoning-driven reranking strategy that explicitly models schema coverage, structural connectivity, and semantic alignment, improving query routing accuracy.
Findings
Outperforms embedding-only and LLM prompting baselines across metrics
Effective in large, domain-overlapping database environments
Addresses ambiguity in natural language queries
Abstract
We address the task of routing natural language queries in multi-database enterprise environments. We construct realistic benchmarks by extending existing NL-to-SQL datasets. Our study shows that routing becomes increasingly challenging with larger, domain-overlapping DB repositories and ambiguous queries, motivating the need for more structured and robust reasoning-based solutions. By explicitly modelling schema coverage, structural connectivity, and fine-grained semantic alignment, the proposed modular, reasoning-driven reranking strategy consistently outperforms embedding-only and direct LLM-prompting baselines across all the metrics.
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Taxonomy
TopicsSemantic Web and Ontologies · Advanced Database Systems and Queries · Natural Language Processing Techniques
