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：摘要：半结构化知识库将文本文档嵌入实体与关系的类型化图中，并支撑产品搜索、学术论文搜索及精准医疗查询等应用。现有的半结构化知识库混合检索系统或仅利用图进行查询扩展，或在全局权重下混合文本分支与结构分支，或依赖微调后的图遍历生成器。我们提出GRASP——一种三阶段半结构化知识库检索框架，统一了基于规划的图检索、与稠密检索器协同的规划条件式融合，以及对融合候选结果进行微调的重排序器。在三个STaRK基准测试中，GRASP在每项指标上均显著提升了现有技术水平，将平均Hit@1从62.0提升至73.9。消融研究与敏感性分析进一步证实了GRASP的有效性与鲁棒性。