Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System

Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques.

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Publication Details

Journal
Future Internet
Published
2025-05-30
DOI
https://doi.org/10.3390/fi17060245
Citations
1
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
2.48
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article

Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System

Raul Valverde, Abid Ali Fareedi, Stéphane Gagnon, Ahmad Ghazawneh
1 citations
Future Internet
Semantic Web and Ontologies
2.48
article

Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System

Raul Valverde, Abid Ali Fareedi, Stéphane Gagnon, Ahmad Ghazawneh
article en
1 citations

Abstract

Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques.

Future InternetVol. 17(6)
Høyskolen Kristiania (NO), Université du Québec en Outaouais (CA), Concordia University (CA), Halmstad University (SE)
Openalex Percentile: Top 10%
Semantic Web and Ontologies
2.48
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