From Text to Databases: attribute grammar as database meta-model

TL;DR

This paper introduces a methodology that uses attribute grammars to transform textual data into structured databases, enabling schema and instance generation from unstructured text, demonstrated through medical case studies.

Contribution

It proposes a novel attribute grammar-based meta-model for structuring textual data into databases, including a formalized evolution process and a proof-of-concept implementation.

Findings

Successfully structured medical textual data into databases

Generated schemas and instances independently of specific database models

Validated the approach with clinical case studies

Abstract

We present a general methodology for structuring textual data, represented as syntax trees enriched with semantic information, guided by a meta-model G defined as an attribute grammar. The method involves an evolution process where both the instance and its grammar evolve, with instance transformations guided by rewriting rules and a similarity measure. Each new instance generates a corresponding grammar, culminating in a target grammar GT that satisfies G. This methodology is applied to build a database populated from textual data. The process generates both a database schema and its instance, independent of specific database models. We demonstrate the approach using clinical medical cases, where trees represent database instances and grammars act as database schemas. Key contributions include the proposal of a general attribute grammar G, a formalization of grammar evolution, and a proof-of-concept implementation for database structuring.