SPIMP-RAG: structure-prior injected message passing for low-budget triple retrieval in LLM-based knowledge graph question answering
Knowledge graph question answering (KGQA) with large language models (LLMs) relies on retrieving a compact set of supporting triples under strict context budgets. However, structure-free or single-stage retrieval can return triples that are semantically relevant in isolation yet insufficiently coordinated as a compact evidence set, which hurts downstream multi-hop reasoning in the low-budget regime. We study this low-budget evidence selection problem under a fixed candidate-subgraph protocol, where the candidate graph is treated as a shared retrieval space for controlled comparison. Our focus is fine-stage triple reranking within this shared candidate space, rather than candidate-subgraph construction. We propose SPIMP-RAG, a coarse-to-fine triple retrieval approach whose key component is Structure-Prior Injected Message Passing (SPIMP), a fine-stage reranker that injects Directional Distance Encoding (DDE) into relation-aware message passing. Starting from a question-centered candidate subgraph, a lightweight DDE+MLP coarse retriever first constructs a compact high-recall candidate set, which is then refined by SPIMP through DDE-guided message routing and adaptive semantic-structural fusion. We further introduce a confidence-weighted weak-supervision scheme to train the coarse scorer and SPIMP reranker from question–answer pairs without requiring gold reasoning paths. Extensive experiments on WebQSP and ComplexWebQuestions show that SPIMP-RAG consistently improves low-budget evidence quality and downstream KGQA performance. In particular, SPIMP-RAG reaches 88.13 Hit@1 / 72.93 F1 on WebQSP and 59.43 Hit@1 / 51.82 F1 on CWQ, and delivers consistent gains under the same candidate-graph protocol. These results support the effectiveness of structure-aware fine reranking for compact evidence selection in LLM-based KGQA.