u/Nervous-Positive-431

Suppose:-

Law A states: Stealing is illegal.

Law B states: Theft of food in case of necessity is permitted.

Lexical search will only capture one of these; since there is no keyword match. Semantic search could potentially see it, but there is a big chance of the right chunk being outside the top-k window.

Knowledge graphs are supposed to solve this issue and sound really good on paper... but does not seem to be performing any better than other RAG techniques.

Is the issue mostly because the LLM at indexing time is not extracting enough complex relationships? Or it is the embedding model not bridging the gap (at indexing and query time)? Like it sounds the closest thing to how a human brain would store (or even update) information.