AI, XR, DevOps, graph theory, knowledge graphs, and digital sovereignty. Deep dives, tutorials, and analysis from across the GraphWiz knowledge base.
An agent can be wrong and still have been right at the time. Bi-temporal memory records what was true and when — but nobody benchmarks what the agent knew at time T.
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.…