# Adaptive Schema Discovery

Graph retrieval for Wikipedia multi-hop question answering no longer needs a large model to run open extraction over the whole corpus: a local GLiNER2 extractor builds the graph under a preset relation schema, then discovers corpus-specific relations from entity pairs the schema left unconnected and injects the resulting triples after the fact. On 1,000 questions each from MuSiQue, 2WikiMultihopQA and HotpotQA, the full pipeline reaches F1 of 0.4577, 0.6637 and 0.7154, above the BGE-based HippoRAG2 baseline; on MuSiQue, adaptive expansion adds 93 relation types and 1,691 triples and raises F1 by 0.0225 over the seed-only graph. Final answer generation still calls GPT-4o-mini.

Graph retrieval for Wikipedia multi-hop question answering no longer needs a large model to run open extraction over the whole corpus: a local GLiNER2 extractor builds the graph under a preset relation schema, then discovers corpus-specific relations from entity pairs the schema left unconnected and injects the resulting triples after the fact. On 1,000 questions each from MuSiQue, 2WikiMultihopQA and HotpotQA, the full pipeline reaches F1 of 0.4577, 0.6637 and 0.7154, above the BGE-based HippoRAG2 baseline; on MuSiQue, adaptive expansion adds 93 relation types and 1,691 triples and raises F1 by 0.0225 over the seed-only graph. Final answer generation still calls GPT-4o-mini.