TLDR Tech / Data
Strategies for Successful Knowledge Graph Projects
Knowledge graph projects frequently fail when treated as mere relational databases, leading to issues like direct SQL-to-RDF translation, row-oriented designs, poor deduplication, and overly ambitious scopes that result in sparse and slow systems. Effective strategies include focusing on specific use cases, implementing staged or federated graphs, utilizing controlled APIs, adhering to SHACL structures, and explicitly tracking provenance. It is recommended to query graphs on demand and maintain a logical model independent of the backend implementation.
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