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Published: 2019-10-24 09:57 | Updated: 2019-10-25 08:35

A new method for finding matches among the various components of ontologies in healthcare, in order to facilitate semantic interoperability

Researchers from Karolinska Institutet and the University of Pireus have presented a new method for finding matches among the various components of ontologies in healthcare in order to facilitate semantic interoperability. The study is published in the International Journal of Medical Informatics.

Close-up portrait of Sokratis Nifakos in blue t-shirt.
Sokratis Nifakos, Department of Learning, Informatics, Management and Ethics, Health Informatics Centre (HIC).

What are the most important results?

"Derived results show that there are cases where an ontology has been matched to a specific HL7 FHIR Resource as a result of its syntactic similarity, whereas the same ontology has been matched to a different HL7 FHIR Resource as a result of its semantic similarity", says Sokratis Nifakos, researcher at the Health Informatics Centre (HIC) at the Department of Learning, Informatics, Management and Ethics.

"Nevertheless, the developed mechanism performed well since its matching results had an exact match with the manual ontology matching results, which are considered as a reference value of high quality and accuracy. Moreover, in order to furtherly investigate the quality of the developed mechanism, it was also evaluated through its comparison with the Alignment API, as well as the non-dominated sorting genetic algorithm (NSGA-III), which provide ontology alignment."

In both cases, the results of all the different implementations were almost identical, proving the developed mechanism’s high efficiency, whereas through the comparison with the NSGA-III algorithm, it was observed that the developed mechanism needs additional improvements, through a potential adoption of the NSGA-III technique.

How did you perform your study?

"The study involved transforming healthcare datasets into HL7 FHIR through their translation into ontologies and matching of healthcare and HL7 FHIR resources ontologies through syntactic and semantic similarities. By creating a XML schema-based transformation of healthcare data into ontologies, conflicts among different ontological concepts could be avoided."

The method also includes the use of a two-stepped ontology matching based on the structure, the syntactic interpretation and the semantic meaning of ontologies.

What are your conclusions?

"The developed mechanism creates new opportunities in conquering the field of healthcare interoperability. Additional work should be performed in evaluating the developed mechanism and updating it with respect to the results that will derive from its comparison with similar ontology matching mechanisms and data of multiple nature", Sokratis explains.

Publication

Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching.
Kiourtis A, Nifakos S, Mavrogiorgou A, Kyriazis D
International Journal of Medical Informatics, 5 October 2019;132():104002

Project

CrowdHEALTH H2020