Why Knowledge Graphs Are Replacing Vector Databases in Scientific Search

Standard RAG fails in production because semantic similarity doesn't equal contextual relevance—a challenge that has plagued Western implementations. A Japanese research team's knowledge graph approach solves this by explicitly modeling entity relationships and enabling reasoning verification through graph traversal, achieving 90% accuracy improvements on scientific tasks. This matters because it reveals a fundamental architectural insight: the problem isn't LLM quality or embeddings, but how retrieval systems are designed.