BridgeKG-RAG: Bridging Wording Gaps in Knowledge-Graph RAG

Knowledge-graph retrieval-augmented generation (KG-RAG) answers questions by linking phrases in the question to entities in a graph of document-grounded facts and retrieving evidence from their neighborhoods. This works when the question uses the same or similar names under which the relevant evidence is organized. However, users may instead mention a near-synonym, a loosely related term, or even a different kind of entity, while the evidence needed to answer is organized under an entity the question never names. We call this the wording gap. The entity mentioned in the question may be a valid graph entity, yet retrieval from its neighborhood can still miss the answer-bearing entity. Neither string similarity nor query rewriting necessarily resolves this mismatch, because the two entities may refer to different things, while traversal over factual edges cannot recover a connection that is absent from the graph. We propose BridgeKG-RAG, which augments the factual graph with typed bridge edges that capture wording relationships between entities. These bridges are used only to expand retrieval starting points and are never treated as answer evidence. Given a question, BridgeKG-RAG selects a small number of bridges to traverse, adds the reached entities to the retrieval set, and then retrieves document-grounded evidence from the factual graph. If the question already names the answer-bearing entity, retrieval remains unchanged. On GapQA, a wording-gap benchmark constructed from Doc2Dial, MultiDoc2Dial, and PrivacyQA, our proposed BridgeKG-RAG substantially improves evidence recall and answer quality.