# Beyond Interestingness: Semantic and Context-Aware Natural Language Query Recommendations for Visual Data Analysis

arXiv: https://arxiv.org/abs/2201.04868 · HTML: https://mlsanity.com/paper/2201.04868/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2022.

## Built on by (most cited first, 6 of 6)

- [PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation](https://mlsanity.com/paper/2307.09036/) (2023)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis](https://mlsanity.com/paper/2301.10385/) (2023)
- [SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model](https://mlsanity.com/paper/2404.13765/) (2024)
- [QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis](https://mlsanity.com/paper/2410.10270/) (2024)
- [DashChat: Interactive Authoring of Performance Dashboard Design Prototypes through Conversation with LLM-Powered Agent](https://mlsanity.com/paper/2504.12865/) (2025)

## Abstract (arXiv)

Recent advances in large language models (LLMs) have made natural language interfaces (NLIs) widely accessible for data exploration, yet analysts who have a broad analytical objective still face the challenge of decomposing it into effective step-by-step queries, especially over unfamiliar, multi-table relational databases. Rather than generating high-level analytical agendas, we investigate how to augment an NLI with semantic- and context-aware next-step query recommendations that act as analytical scaffolding for relational database exploration. Our approach goes beyond interestingness-only methods by jointly integrating semantic relevance, data interestingness, and context coherence to guide exploration toward coherent, topic-focused analyses and potentially insightful subsets. We evaluate QRec-NLI with NL2SQL benchmarking, LLM-enhanced description validation, agentic comparisons against interestingness-only and LLM-based prompting baselines, and a 12-participant user study. In the agentic comparison, QRec-NLI yields more topically relevant and locally coherent query sequences than both baselines. In the user study against the interestingness-only baseline, it receives stronger ratings for insight-generation support and decision support.