GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases
For applications like product search and precision medicine that rely on semi-structured knowledge bases, GRASP provides a significant improvement in retrieval accuracy, though it is an incremental advancement over existing hybrid retrieval methods.
GRASP introduces a three-stage retrieval framework for semi-structured knowledge bases that combines plan-guided graph retrieval, adaptive fusion with dense retrieval, and fine-tuned reranking, achieving state-of-the-art results on the STaRK benchmarks with average Hit@1 improving from 62.0 to 73.9.
Semi-structured knowledge bases (SKBs) embed textual documents in a typed graph of entities and relations, and underpin applications such as product search, academic paper search, and precision-medicine inquiries. Existing hybrid retrieval systems on SKBs either use the graph only for query expansion, mix textual and structural branches under a global weighting, or rely on fine-tuned graph-traversal generators. We present GRASP, a three-stage SKB retrieval framework unifying plan-based graph retrieval, plan-conditioned fusion with a dense retriever, and a fine-tuned reranker over the fused candidates. GRASP substantially advances the state of the art on every metric across the three STaRK benchmarks, lifting average Hit@1 from 62.0 to 73.9. Ablation and sensitivity studies further confirm the effectiveness and robustness of GRASP.