We introduce an automated method for structuring textual data into a model-agnostic schema, enabling alignment with any database model. It generates both a schema and its instance. Initially, textual data is represented as semantically enriched syntax trees, which are then refined through iterative tree rewriting and grammar extraction, guided by the attribute grammar meta-model \metaG. The applicability of this approach is demonstrated using clinical medical cases as a proof of concept.
@article{arxiv.2512.11403,
title = {Bridging Textual Data and Conceptual Models: A Model-Agnostic Structuring Approach},
author = {Jacques Chabin and Mirian Halfeld Ferrari and Nicolas Hiot},
journal= {arXiv preprint arXiv:2512.11403},
year = {2025}
}