## 6 Conclusions and Future Directions  
This research makes significant contributions, primarily by bridging Ontology Engineering (OE) paradigms with the Design Science Research (DSR) discipline to develop digital artifacts that address real-time challenges and support the evolution of new types of IS in the healthcare domain. We introduced a customized hybrid method, Ontology-based Design Science Research Engineering (ODSRE), which integrates DSR activities with OE principals, incorporating elements from Methontology and Tove methodologies to develop ontological model digital artifacts. This contribution encourages IS practitioners to align with the software engineering perspective closely, creating new kinds of design artifacts (e.g., KGs) and leveraging _Design Science_ (DS) theories for IS development across various domains, including Health Information Systems (HISs). Second, this research demonstrates the effectiveness of utilizing the Ontop platform with the VKG approach alongside an FVKG to address Data Integration (DI) and Semantic Interoperability (SI) bottlenecks in federated HIS. The proposed framework enhances DI&SI, providing a platform as a unified virtual global view of diverse data sources and allowing for querying based on domain-centric concepts rather than underlying data structures and formats. Figure 12: Ontop SPARQL endpoint for information retrieval against sample query. Additionally, FVKG minimizes data transfer, reduces latency rate, ensures data freshness, and improves overall data access efficiency and reliability. As a strategic approach to building a robust ontological model that captures domain-specific knowledge, it enables Ontology-based Data Access (OBDA) and helps in semantic alignment for ontology-driven applications. We utilized a customized hybrid framework FVKG to facilitate domain ontological model construction and data fetching and mapping using the Ontop platform from different distributed data sources or models within the healthcare domain. We are fully motivated and looking ahead. Several areas of future research can be improved by the proposed FVKG framework and its applications in healthcare settings. As a future direction, we are fully committed to planning implementation with real-world datasets from medical institutions and deploying proposed FVKGs to see the mapping phenomenon using the Ontop platform. Enhanced semantic mapping methodsWe plan to develop advanced semantic mapping algorithms integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques for improved ontology alignment and schema matching across heterogeneous healthcare data sources. Optimized large-scale federated systemsWe plan to investigate various strategies for optimizing query processing, caching, and indexing mechanisms to foster the performance of FVKG and scalability in large-scale deployments. Automated ontology evolution and adaptationWe plan to implement dynamic ontology evolution mechanisms to automatically update and refine domain ontologies in response to changes in healthcare standards and data sources. Hybrid AI-driven data integrationWe will explore the integration of hybrid AI models to automate data harmonization, anomaly detection, and predictive analytics within the federated healthcare environment. Interoperability with emerging healthcare standardsWe plan to ensure compatibility with evolving standards such as FHIR ([[https://build.fhiur.org/](https://build.fhiur.org/)]([https://build.fhiur.org/](https://build.fhiur.org/))), SNOMED CT ([[https://www.snomed.org/](https://www.snomed.org/)]([https://www.snomed.org/](https://www.snomed.org/)) (accessed on 21 May 2025)), and HL7 ([[https://www.hl7.org/fhiur/overview.html](https://www.hl7.org/fhiur/overview.html)]([https://www.hl7.org/fhiur/overview.html](https://www.hl7.org/fhiur/overview.html)) (accessed on 21 May 2025)) to improve cross-platform interoperability and regulatory compliance.