# Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity

arXiv: https://arxiv.org/abs/1906.10322 · HTML: https://mlsanity.com/paper/1906.10322/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [CORNET: Learning Table Formatting Rules By Example](https://mlsanity.com/paper/2208.06032/) (2022)
- [Beyond Closed-Pool Video Retrieval: A Benchmark and Agent Framework for Real-World Video Search and Moment Localization](https://mlsanity.com/paper/2602.10159/) (2026)
- [Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models](https://mlsanity.com/paper/2412.07214/) (2024)
- [Tabularis Formatus: Predictive Formatting for Tables](https://mlsanity.com/paper/2508.11121/) (2025)

## Abstract (arXiv)

Traditional relational data interfaces require precise structured queries over potentially complex schemas. These rigid data retrieval mechanisms pose hurdles for non-expert users, who typically lack language expertise and are unfamiliar with the details of the schema. Query by Example (QBE) methods offer an alternative mechanism: users provide examples of their intended query output and the QBE system needs to infer the intended query. However, these approaches focus on the structural similarity of the examples and ignore the richer context present in the data. As a result, they typically produce queries that are too general, and fail to capture the user's intent effectively. In this paper, we present SQuID, a system that performs semantic similarity-aware query intent discovery. Our work makes the following contributions: (1) We design an end-to-end system that automatically formulates select-project-join queries in an open-world setting, with optional group-by aggregation and intersection operators; a much larger class than prior QBE techniques. (2) We express the problem of query intent discovery using a probabilistic abduction model, that infers a query as the most likely explanation of the provided examples. (3) We introduce the notion of an abduction-ready database, which precomputes semantic properties and related statistics, allowing SQuID to achieve real-time performance. (4) We present an extensive empirical evaluation on three real-world datasets, including user-intent case studies, demonstrating that SQuID is efficient and effective, and outperforms machine learning methods, as well as the state-of-the-art in the related query reverse engineering problem.