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.

翻译：半结构化知识库（SKBs）将文本文档嵌入实体与关系的类型化图中，支撑着产品搜索、学术论文检索及精准医学查询等应用。现有针对SKBs的混合检索系统或仅利用图进行查询扩展，或通过全局加权混合文本与结构分支，亦或依赖微调的图遍历生成器。我们提出GRASP——一种三阶段SKB检索框架，统一了基于规划的图检索、基于规划条件与密集检索器融合，以及对融合候选项的微调重排序。在STaRK三个基准测试的所有指标上，GRASP显著推进了现有技术水平，将平均Hit@1从62.0提升至73.9。消融实验与敏感性研究进一步证实了GRASP的有效性与鲁棒性。