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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

Towards a Health Data Knowledge Graph.

Jane-Ly Buhvestova¹, Gunnar Piho¹, Erki Eessaar¹

¹eMedLab at Tallinn University of Technology, Tallinn, Estonia.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

This summary is machine-generated.

Researchers are developing a formal language for health data semantics, inspired by ABC4HEDA and MOF standards, to bridge interoperability gaps and unify medical knowledge graphs.

Keywords:

Knowledge graph (KG)health data semantic interoperability heterogeneous systems

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Area of Science:

Health Informatics
Computer Science
Knowledge Representation

Background:

Persistent semantic interoperability challenges exist despite current standards like HL7 FHIR and SNOMED CT.
Existing standards do not fully resolve the complexities of formalizing health data semantics.
A need exists for a robust framework to enable semantically unified medical knowledge.

Purpose of the Study:

To investigate the development of a formal language for specifying health data semantics.
To leverage the principles of the Archetype-based Computing for Health Data (ABC4HEDA) model.
To structure this language according to the Meta-Object Facility (MOF) standard.

Main Methods:

Employed a design science research approach.
Investigated the foundational principles of the ABC4HEDA model.
Utilized the Meta-Object Facility (MOF) standard for structural organization.

Main Results:

Presented a conceptual basis for a formal semantic specification language.
Demonstrated preliminary results from ongoing research.
The developed conceptual framework aims to support future semantic unification.

Conclusions:

The proposed conceptual basis offers a pathway toward addressing semantic interoperability gaps.
Future work will focus on implementing this language within the TermX platform for distributed healthcare data exchange.
This research contributes to advancing semantically unified medical knowledge graphs.