Healthcare pioneered structured knowledge long before computers, creating shared disease classifications and data standards that enabled modern medicine. This foundation evolved into sophisticated knowledge graphs—layered systems where ontologies define relationships, controlled vocabularies catalog entities, and clinical observations provide evidence. These structures transformed medicine from bloodletting and mercury treatments to targeted drug design and real-time global learning.

Knowledge graphs solve critical scaling problems: fragmented search across systems, complex discovery, knowledge reuse, and explainable decision support. Healthcare's mature domain graphs let physicians understand drug side effects across borders and aggregate insights from millions of clinical encounters. The approach enables network medicine techniques that identify disease mechanisms and therapeutic targets by analyzing millions of connected nodes and edges.

The Chan Zuckerberg Initiative's goal to cure all diseases by 2100 depends on these knowledge structures. Unlike simple databases, knowledge graphs encode explicit facts—'Salvarsan inhibits Treponema pallidum' and 'Treponema pallidum causes syphilis'—allowing systems to infer that Salvarsan treats syphilis. This three-part series explores how healthcare mastered knowledge graphs and what other industries can learn.