Practical guidelines for designing, modeling, and maintaining knowledge graphs in production - ontology design, data quality, schema evolution, and governance patterns.
Knowledge Graph Best Practices Guide | Data AI Hub
TL;DR
Start with use-case-driven modeling — define the questions your graph must answer before designing the full ontology. A knowledge graph is a semantic model; the graph database is just where you store it.
Entity resolution is non-negotiable — duplicate nodes destroy trust faster than missing data. Resolution is a platform capability, not an ETL afterthought.
Schema evolution is continuous — plan for additive changes, versioning, and backward-compatible migrations from day one. Ontology changes follow the same rigor as API versioning.
Data quality gates belong in the pipeline — validate at ingest with SHACL or constraints, not after users discover bad data in production.
Governance beats technology — assign stewards per domain, version ontologies in Git, and review schema changes like API changes. Neo4j, Stardog, GraphDB, Neptune, and Jena are interchangeable compared to modeling discipline.