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Grounding LLM Reasoning with Knowledge Graphs
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This method proposes an LLM-based agent that interacts with a Knowledge Graph in an interleaved sequence of thought, action, and retrieved data. It uses predefined actions like RetrieveNode, NodeFeature, NeighborCheck, and NodeDegree to systematically ground LLM reasoning steps in structured knowledge, thus enabling synergistic reasoning.
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This method integrates LLM language generation with structured graph retrieval by automatically extracting entities from LLM-generated thoughts. It then guides a multi-step search and prune pipeline over the KG, using LLM prompts to filter relevant relations and neighboring entities, enabling dynamic and structured exploration of the KG for reasoning.
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Automatic Graph Exploration (LLM+KG Interaction Method)
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