AI-assisted JSON Schema Creation and Mapping

TL;DR

This paper introduces a hybrid AI approach that combines large language models with deterministic methods to facilitate JSON Schema creation, modification, and mapping from natural language, making data modeling accessible to non-experts.

Contribution

It presents a novel hybrid system integrating LLMs and deterministic techniques for schema creation and mapping, implemented in an open-source tool for easier data integration.

Findings

Effective schema creation from natural language inputs.

Reliable schema mapping across heterogeneous data formats.

Demonstrated application in chemistry data management.

Abstract

Model-Driven Engineering (MDE) places models at the core of system and data engineering processes. In the context of research data, these models are typically expressed as schemas that define the structure and semantics of datasets. However, many domains still lack standardized models, and creating them remains a significant barrier, especially for non-experts. We present a hybrid approach that combines large language models (LLMs) with deterministic techniques to enable JSON Schema creation, modification, and schema mapping based on natural language inputs by the user. These capabilities are integrated into the open-source tool MetaConfigurator, which already provides visual model editing, validation, code generation, and form generation from models. For data integration, we generate schema mappings from heterogeneous JSON, CSV, XML, and YAML data using LLMs, while ensuring scalability and reliability through deterministic execution of generated mapping rules. The applicability of our work is demonstrated in an application example in the field of chemistry. By combining natural language interaction with deterministic safeguards, this work significantly lowers the barrier to structured data modeling and data integration for non-experts.