Design and Implementation of a Knowledge Graph for Integrating Global Health Data Using Neo4j

Global health research requires the integration of diverse datasets and continuous updates to enable more effective monitoring and interpretation of global health trends. However, traditional tabular datasets often fall short in accommodating multiple datasets with heterogeneous schemas and evolving structures, and they hinder the efficient updating of such datasets once integrated. Graph databases offer a natural solution to these challenges by providing a flexible data structure that can explicitly represent complex relationships and accommodate incremental updates without requiring modifications to existing data. This paper presents an initial attempt to apply graph database technology to global health research by proposing a structured roadmap for constructing a Neo4j-based knowledge graph from heterogeneous data sources. The application of knowledge graphs in global health research enables comprehensive, up-to-date representation of complex datasets, facilitates direct insights into multifaceted relationships, supports multi-perspective analyses, and establishes a foundation for scalable and analytically robust research in the field.