Generating Relational Database using Ontology Review

Khnaisser, Christina; Lavoie, Luc; Burgun, Anita; Éthier, Jean-François

doi:10.14569/ijacsa.2018.090620

Cited by 2 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literature suggested several methods to derive a relational data model from an ontology. A literature review was conducted in mid-2018 to explore methods published after 2010 describing RDB generation from an ontology 17 . A list of 23 criteria was defined to evaluate and compare the 10 most relevant papers.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many approaches, that convert an ontology to a data model, have been proposed but limitations have been identified in the coverage of ontological constructs and the correctness of the relational databases generated as demonstrated in the survey. 14 First, including temporal concepts in an ontology requires substantial design work, decreases reasoning efficiency and may cause semantic errors due to the complexity of axioms. 9,15 Second, designing a data model using an ontology requires formal algorithms to guarantee semantic conservation 11,16 ; and third, modelling and implementing a temporal data model in the database require advanced understanding of temporal representation, programming skills and design tools.…”

Section: Introductionmentioning

confidence: 99%

Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures

Khnaisser,

Looten,

Lavoie

et al. 2024

Health Informatics J

Self Cite

View full text Add to dashboard Cite

Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model. The framework is based on: OntoRela an ontology-driven database modelling approach and Unified Historicization Framework a temporal database modelling approach. The method was applied to hospital organizational structures to show the impact of tracking organizational changes on data quality assessment, healthcare activities and data access rights. The paper demonstrated the usefulness of an ontology to provide a formal, interoperable, and reusable definition of entities and their relationships, as well as the adequacy of the temporal database to store, trace, and query data over time.

show abstract

Generating Relational Database using Ontology Review

Cited by 2 publications

References 16 publications

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

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

Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures

Contact Info

Product

Resources

About