GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases
arXiv:2605.30237v1 Announce Type: cross Abstract: 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 retri
The continuous evolution of AI and information retrieval systems necessitates more sophisticated methods for navigating complex knowledge bases, particularly as data scales and becomes more integrated.
This development in graph retrieval directly impacts the efficiency and accuracy of AI applications built on semi-structured knowledge bases, potentially improving search, reasoning, and decision-making capabilities across various domains.
Retrieval systems operating on large, semi-structured knowledge bases can now leverage more adaptive and plan-guided methodologies, moving beyond simpler hybrid approaches or fine-tuned generators.
- · AI/ML researchers
- · Companies with large knowledge graphs
- · Users of advanced search engines
- · Data scientists
- · Legacy information retrieval systems
- · Basic keyword search solutions
Improved performance in applications relying on complex data retrieval, such as product search and academic research.
Accelerated development of AI agents capable of more nuanced understanding and interaction with vast information stores.
Enhanced automation of workflows that require parsing and synthesizing information from diverse, interconnected data sources.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG