Data-Aware Socratic Query Refinement in Database Systems

2025-08-07

AI Summary

Natural language queries often suffer from semantic ambiguity, leading to inaccurate parsing in database systems. Method: This paper proposes a data-aware Socratic interactive clarification framework that models conversational clarification as a first-class operator within database systems—enabling the system to proactively and selectively initiate semantic disambiguation questions. Leveraging joint modeling of linguistic ambiguity, schema-matching confidence, and multi-backend (relational/vector) execution costs, we design a cost-benefit-driven question-selection mechanism. Optimal questions are chosen by jointly optimizing semantic relevance, catalog information gain, and execution-cost reduction potential. Results: Experiments on three benchmark datasets demonstrate substantial improvements in query accuracy while limiting average interaction rounds to 1.2–1.8. The framework achieves a balanced trade-off between precision and efficiency, establishing a new paradigm for collaborative natural language database interaction.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Natural Language Processing: Question Answering
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

In this paper, we propose Data-Aware Socratic Guidance (DASG), a dialogue-based query enhancement framework that embeds linebreak interactive clarification as a first-class operator within database systems to resolve ambiguity in natural language queries. DASG treats dialogue as an optimization decision, asking clarifying questions only when the expected execution cost reduction exceeds the interaction overhead. The system quantifies ambiguity through linguistic fuzziness, schema grounding confidence, and projected costs across relational and vector backends. Our algorithm selects the optimal clarifications by combining semantic relevance, catalog-based information gain, and potential cost reduction. We evaluate our proposed framework on three datasets. The results show that DASG demonstrates improved query precision while maintaining efficiency, establishing a cooperative analytics paradigm where systems actively participate in query formulation rather than passively translating user requests.

Problem

• Resolves ambiguity in natural language database queries
• Optimizes dialogue-based query clarification for cost efficiency
• Improves query precision while maintaining system efficiency

Innovation

• Dialogue-based query enhancement framework
• Optimizes clarification questions via cost reduction
• Quantifies ambiguity using linguistic and schema metrics