Bridging Textual Data and Conceptual Models: A Model-Agnostic Structuring Approach

TL;DR

This paper presents an automated, model-agnostic approach for converting textual data into structured schemas and instances, using semantic trees and iterative refinement, demonstrated on clinical medical data.

Contribution

It introduces a novel method combining semantic syntax trees and attribute grammar refinement for flexible data structuring across models.

Findings

Effective structuring of clinical data demonstrated

Schema and instance generation achieved

Applicable to diverse database models

Abstract

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.