From Syntactic to Semantic Interoperability Using a Hyperontology in the Oncology Domain

Area of Science:

• Biomedical Informatics
• Health Data Science
• Ontology Engineering

Background:

• Healthcare data integration faces challenges due to syntactic and semantic heterogeneity.
• Existing standards like OMOP and FHIR address syntactic interoperability but not semantic complexity.
• Semantic interoperability is essential for leveraging distributed big data in oncology.

Purpose of the Study:

• To propose an ontological approach for semantic interoperability in cancer imaging data.
• To support the EUCAIM project by enabling data integration across heterogeneous repositories.
• To leverage a semantically well-founded Hyperontology for the oncology domain.

Main Methods:

• Development of a Hyperontology tailored for the oncology domain.
• Application of the ontology to address semantic heterogeneity in cancer image data models.
• Integration of the ontological approach within the EUCAIM project framework.

Main Results:

• Demonstrated the feasibility of using a Hyperontology for semantic interoperability.
• Facilitated the integration of distributed big data repositories with heterogeneous cancer image data.
• Established a semantically unified approach for oncology data.

Conclusions:

• The proposed ontological approach effectively supports semantic interoperability for heterogeneous cancer imaging data.
• This methodology enhances data integration and analysis capabilities within the EUCAIM project.
• Ontologies are critical for overcoming semantic disparities in complex healthcare big data.