Other industries can learn valuable lessons from healthcare’s leadership in knowledge graphs, not because healthcare was the first to adopt new technology, but because it invested for centuries in creating shared meaning across complex domains. Long before AI or modern data platforms, medicine built consensus on what exists, how to name entities, how to generate evidence, how to exchange data, and how to enforce alignment through regulation and collaboration. These foundational practices enabled the accumulation of reliable, reusable knowledge—something other industries are now beginning to emulate. One key takeaway is the importance of shared ontologies—formal representations of concepts and their relationships. Healthcare has developed thousands of domain-specific ontologies, such as those for anatomy (Uberon), genes (Gene Ontology), and chemicals (ChEBI), as well as cross-domain standards like Schema.org and QUDT. These are built using W3C standards like OWL, SHACL, and SKOS, ensuring interoperability and long-term sustainability. Upper ontologies like BFO and SUMO provide high-level frameworks for modeling reality consistently across domains. Other industries are following suit: the European Legislation Identifier (ELI) Ontology standardizes legal concepts across EU countries, enabling semantic interoperability in legislation. The Environment Ontology (ENVO) models ecosystems and environmental processes, demonstrating how community-driven ontologies can become essential infrastructure. In finance, the Financial Industry Business Ontology (FIBO) establishes common definitions for financial instruments, contracts, and entities, allowing firms to compete on products rather than semantics. Healthcare also treats controlled vocabularies not as project-specific tools but as critical infrastructure. Standards like SNOMED CT, ICD-11, MedDRA, RxNorm, and UniProt provide consistent, machine-readable definitions for diseases, procedures, drugs, and biological entities. These are integrated into large-scale knowledge graphs like SPOKE and Open Targets, enabling powerful data integration. Other sectors can replicate this by building open, shared catalogs of core entities—such as companies, financial instruments, or construction materials—and publishing them as reusable datasets. Empirical observation drives structure in healthcare. Clinical trials follow strict standards like CDISC, which define how measurements, outcomes, and adverse events are recorded. This ensures that findings are reproducible and cumulative. Similar approaches are emerging in other fields: climate science uses standardized observation frameworks, and supply chain tracking is adopting structured data models to improve transparency. Interoperability is another cornerstone. HL7 FHIR standardizes how clinical data is exchanged between systems, enabling seamless integration across hospitals and platforms. Other industries are adopting similar models: in finance, the ISO 20022 standard is transforming how financial messages are structured and shared, while in construction, the Industry Foundation Classes (IFC) standard enables data exchange across building design and engineering tools. Regulation has played a crucial role in enforcing semantic alignment. The FDA mandates the use of standards like CDISC and MedDRA, ensuring consistency in clinical trial reporting and drug safety data. While finance and aviation also face regulatory requirements, they lack the same level of mature, shared semantic infrastructure—yet the path is clear. Healthcare also separates pre-competitive semantics from competitive advantage. The Pistoia Alliance brings together pharmaceutical competitors to develop shared ontologies and data standards, accelerating innovation without compromising business interests. This model is replicable in other sectors, from energy to logistics. Public funding has been vital. NIH support has sustained core biomedical ontologies and vocabularies. Other industries can create similar public-private partnerships to fund shared semantic infrastructure. Finally, anchoring meaning in open standards like RDF, OWL, and SHACL ensures longevity and vendor neutrality. Knowledge built on open standards can evolve with technology and remain usable for decades. Healthcare’s success wasn’t due to AI—it was due to centuries of externalizing meaning. Knowledge graphs don’t create agreement—they make it computable, reusable, and scalable. Other industries don’t need to follow the same path, but they can adopt these principles: agree on what exists, treat reference data as infrastructure, let evidence guide structure, use regulation and collaboration, fund semantics as a public good, and anchor meaning in open standards. The result? Knowledge that grows, not fragments.