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A knowledge graph is only as trustworthy as its identity layer. When the same real-world entity arrives from many sources spelled differently, you get duplicates that silently poison every downstream query. How to do entity resolution and canonical identity at graph scale — blocking, similarity, and merges you can audit.
RAG systems are evolving rapidly, but not all RAG is created equal. This article charts the journey from naive vector search to agentic, cost-optimized knowledge integration, with practical guidance on navigating the cost-accuracy trade-off for enterprise deployments.
X-KGRank unifies structural collaborative filtering with LLM-based explanation. A 1.5B parameter model matches a 7B model on explanation quality — but fabricates facts more often when not grounded.
Persona-memory systems suffer from a validity gap — personas drift without traceable evidence. PGMem solves this with a heterogeneous graph that keeps every signal traceable to its source events.
Nonaka and Takeuchi's SECI model remains the definitive framework for organisational knowledge creation.…