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GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases

A plan-guided graph retrieval method for semi-structured knowledge bases.

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GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases

arXiv
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The paper proposes GRASP, a plan-guided graph retrieval system that uses adaptive fusion and reranking to improve the accuracy of retrieving relevant information from semi-structured knowledge bases.

The approach combines a planning module with a retrieval module to adaptively select relevant subgraphs and fuse their representations. This allows for more accurate and efficient retrieval of relevant information.

Abstract

The paper proposes GRASP, a plan-guided graph retrieval system that uses adaptive fusion and reranking to improve the accuracy of retrieving relevant information from semi-structured knowledge bases. The approach combines a planning module with a retrieval module to adaptively select relevant subgraphs and fuse their representations. This allows for more accurate and efficient retrieval of relevant information.

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graph-retrievalsemi-structured-knowledge-basesadaptive-fusionrerankingKnowledge GraphsContent EngineeringAI AgentsRetrieval & RAG
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