# Large Language Models for JSON Schema Discovery

arXiv: https://arxiv.org/abs/2407.03286 · HTML: https://mlsanity.com/paper/2407.03286/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2024.

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

- [LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models](https://mlsanity.com/paper/2504.00752/) (2025)
- [A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data](https://mlsanity.com/paper/2601.17717/) (2026)
- [Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance](https://mlsanity.com/paper/2502.15604/) (2025)
- [SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions](https://mlsanity.com/paper/2607.27955/) (2026)

## Abstract (arXiv)

Semi-structured data formats such as JSON have proved to be useful data models for applications that require flexibility in the format of data stored. However, JSON data often come without the schemas that are typically available with relational data. This has resulted in a number of tools for discovering schemas from a collection of data. Although such tools can be useful, existing approaches focus on the syntax of documents and ignore semantic information. In this work, we explore the automatic addition of meaningful semantic information to discovered schemas similar to information that is added by human schema authors. We leverage large language models and a corpus of manually authored JSON Schema documents to generate natural language descriptions of schema elements, meaningful names for reusable definitions, and identify which discovered properties are most useful and which can be considered "noise". Our approach performs well on existing metrics for text generation that have been previously shown to correlate well with human judgement.