WHY KNOWLEDGE GRAPH PROJECTS FAIL — AND HOW TO MAKE THEM SUCCEED

Summary from the TLDR Data newsletter

Knowledge graphs often fail when treated like relational databases: direct SQL-to-RDF translation, row-oriented design, weak deduplication, and oversized scopes create sparse, slow systems. Better practice starts with specific use cases, staged or federated graphs, controlled APIs, SHACL structure, and explicit provenance. Query graphs on demand and keep the logical model independent of backend.