Semantic Knowledge Graphs for Scalable Knowledge Discovery and Acquisition in Public Health

Textual medical knowledge for the public health sector exists as data lakes and repositories across diverse distributed computing systems. However, access to these is impacted by diverse challenges including the need for data aggregation, lack of appropriate contextual basis for data and challenges with scaling the data for widespread availability and accessibility in usable formats. Hence, this research investigates a model for organizing and integrating medical information into semantic knowledge graphs. This is based on a design patterns and semantic standards methodology to facilitate modular, yet cohesive system components for designing and developing semantic knowledge graphs for scalable knowledge discovery and acquisition in the public health sector. Based on these, a conceptual framework for achieving the proposed solution is developed, incorporating design patterns best practices with semantic standards and technologies such as knowledge modelling and graph schemas. Adoption of the framework for practical implementation of public health knowledge portals presents a means of addressing the challenges and fostering improved state of public healthcare systems.