Paper Type
Complete
Paper Number
PACIS2025-1541
Description
This research uses design science research methodology to present a comparative synthesis of balancing between materialized and virtualized knowledge graph approaches articulating domain-centric monolithic federated virtual knowledge graphs (FVKGs) leveraging data mapping techniques in healthcare information systems (HIS). Materialized knowledge graphs enable fast query execution by physical instantiation of graph structures. In contrast, virtualized knowledge graphs using the Ontop virtual system to fetch real-time, on-demand querying over disparate data sources. This research thoroughly examines the strengths, shortcomings, and suitability of usage in diverse HIS applications landscape. Key critical factors such as scalability, performance, maintenance overhead, and query efficiency are analyzed to evaluate the effectiveness of each approach. The authors use a federated ontological model leveraging disparate static data models in materialization and support real-time, dynamic changing datasets in virtualization to construct FVKGs. The experimental findings showcase ontological artifacts, balancing between approaches, enhancing data integration and interoperability capabilities to optimize HIS.
Recommended Citation
Fareedi, Abid Ali and Ghazawneh, Ahmad, "Balancing Materialized and Virtualized Knowledge Graphs Articulation in Healthcare Information Systems: A Comparative Synthesis" (2025). PACIS 2025 Proceedings. 9.
https://aisel.aisnet.org/pacis2025/ishealthcare/ishealthcare/9
Balancing Materialized and Virtualized Knowledge Graphs Articulation in Healthcare Information Systems: A Comparative Synthesis
This research uses design science research methodology to present a comparative synthesis of balancing between materialized and virtualized knowledge graph approaches articulating domain-centric monolithic federated virtual knowledge graphs (FVKGs) leveraging data mapping techniques in healthcare information systems (HIS). Materialized knowledge graphs enable fast query execution by physical instantiation of graph structures. In contrast, virtualized knowledge graphs using the Ontop virtual system to fetch real-time, on-demand querying over disparate data sources. This research thoroughly examines the strengths, shortcomings, and suitability of usage in diverse HIS applications landscape. Key critical factors such as scalability, performance, maintenance overhead, and query efficiency are analyzed to evaluate the effectiveness of each approach. The authors use a federated ontological model leveraging disparate static data models in materialization and support real-time, dynamic changing datasets in virtualization to construct FVKGs. The experimental findings showcase ontological artifacts, balancing between approaches, enhancing data integration and interoperability capabilities to optimize HIS.
Comments
Healthcare