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

2025-12-12

AI Summary

Automatically aligning unstructured text—such as clinical case notes—with diverse, heterogeneous database schemas remains challenging due to rigid schema-binding assumptions in existing approaches. Method: We propose a model-agnostic text structuring method grounded in an attribute grammar metamodel (MetaG), which constructs semantically enriched parse trees. Through iterative tree rewriting and extraction, the method jointly generates executable semantic schemas and their corresponding instances—without requiring prior knowledge of target data models. Contribution/Results: Our key innovation lies in decoupling text understanding from database schema design, enabling seamless adaptation to heterogeneous storage backends—including relational, graph, and document databases. Empirical evaluation on clinical texts demonstrates accurate generation of cross-model-compatible schemas and instances, significantly overcoming limitations of conventional schema-bound paradigms. The approach achieves robust schema generalization and instance fidelity across disparate target models, establishing a foundation for flexible, database-agnostic text-to-structure translation.

Technology Category

• Machine Learning: Structured Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Agentic search
• Web Mining and Content Analysis: Bridging structured and unstructured data

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.

Problem

• Automates structuring textual data into model-agnostic schemas
• Generates both schema and instance from enriched syntax trees
• Demonstrates approach with clinical medical cases as proof

Innovation

• Automated method structures text into model-agnostic schema
• Uses enriched syntax trees refined via iterative rewriting
• Applies attribute grammar meta-model for schema generation