CC BY-NC-ND 4.0 · Methods Inf Med
DOI: 10.1055/a-1877-9498

OntoRelα: ontology to relational data model

Christina Khnaisser

1   Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Canada (Ringgold ID: RIN7321)

,

Luc Lavoie

2   GRIIS, Département d'informatique, Université de Sherbrooke, Sherbrooke, Canada (Ringgold ID: RIN7321)

,

Benoit Fraikin

3   GRIIS, Université de Sherbrooke, Sherbrooke, Canada (Ringgold ID: RIN7321)

,

Adrien Barton

4   MELODI, IRIT, Toulouse, France (Ringgold ID: RIN54816)

,

Samuel Dussault

3   GRIIS, Université de Sherbrooke, Sherbrooke, Canada (Ringgold ID: RIN7321)

,

Anita Burgun

5   INSERM UMRS 1138 team 22, Université de Paris, Paris, France (Ringgold ID: RIN555089)

,

Jean-François Ethier

6   Department of Medicine, Université de Sherbrooke, Sherbrooke, Canada (Ringgold ID: RIN7321)

› Author AffiliationsSupported by: Fonds de Recherche du Québec - Nature et Technologies
Supported by: Centre de recherche interdisciplinaire en informatique de la santé de l’Université de Sherbrooke
Supported by: Health Data Research Network Canada
Supported by: Unité Soutien SRAP du Québec
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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 its full semantic remains a challenging task. Objectives. This paper addresses the question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and its semantic to support various applications? Method. This paper 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 paper (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 , a prototype developed to demonstrate the conversion rules. Conclusion. This work is a first step towards 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.

Publication History

Received: 19 January 2022

Accepted after revision: 11 June 2022

Accepted Manuscript online:
16 June 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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