Seunghui Jwa

Knowledge-graph question answering usually ranks facts by relevance and packs them into the prompt, but relevance guarantees neither sufficiency nor consistency. This method uses relation knowledge, either hand-written rules for what relations mean and how they compose or a mapping learned from training questions annotated with relation paths, to search the graph for a compact fact set that derives an answer, then applies a derivability check for whether an answer follows and a consistency check for whether facts conflict, with a validation-chosen gate deciding when the derived answer replaces the LLM's. On WebQSP, seven open LLMs read these contexts, 14.7 facts on average, at 10.2 exact-match points above SubgraphRAG's 100 facts.

Knowledge-graph question answering usually ranks facts by relevance and packs them into the prompt, but relevance guarantees neither sufficiency nor consistency. This method uses relation knowledge, either hand-written rules for what relations mean and how they compose or a mapping learned from training questions annotated with relation paths, to search the graph for a compact fact set that derives an answer, then applies a derivability check for whether an answer follows and a consistency check for whether facts conflict, with a validation-chosen gate deciding when the derived answer replaces the LLM's. On WebQSP, seven open LLMs read these contexts, 14.7 facts on average, at 10.2 exact-match points above SubgraphRAG's 100 facts.