PDF icon Download PDF
Open Access
CC BY 4.0 · Methods Inf Med 2022; 61(S 02): e73-e88
DOI: 10.1055/a-1877-9498
Original Article

Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications

Christina Khnaisser 
1   GRIIS, Université de Sherbrooke, Sherbrooke, Canada
,
Luc Lavoie
1   GRIIS, Université de Sherbrooke, Sherbrooke, Canada
,
Benoit Fraikin
1   GRIIS, Université de Sherbrooke, Sherbrooke, Canada
,
Adrien Barton 
2   IRIT-MELODI, CNRS, Toulouse, France
,
Samuel Dussault 
1   GRIIS, Université de Sherbrooke, Sherbrooke, Canada
,
Anita Burgun 
3   INSERM UMRS 1138 Team 22, Université de Paris, Paris, France
,
Jean-François Ethier 
1   GRIIS, Université de Sherbrooke, Sherbrooke, Canada
› Author Affiliations
› Further Information
  Permissions and Reprints
Preview

Abstract

Background A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with their full semantic remains a challenging task.

Objectives This article addresses the following question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and their semantic to support various applications?

Method This article describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing.

Results This article (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents O n t o R e l a , a prototype developed to demonstrate the conversion rules.

Conclusion This work is a first step toward automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out.

Keywords

heterogeneous data - information - data integration - ontologies - management

Ethical Considerations

This research does not involve human subjects.




Publication History

Received: 19 January 2022

Accepted: 11 June 2022

Accepted Manuscript online:
16 June 2022

Article published online:
03 December 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany