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ROG: Retrieval-Augmented LLM Reasoning for Complex First-Order Queries over Knowledge Graphs
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ROG is a retrieval-augmented framework that combines query-aware neighborhood retrieval from Knowledge Graphs with Large Language Model chain-of-thought reasoning to answer complex First-Order Logic queries. It explicitly decomposes multi-operator queries, grounds each step in compact, retrieved KG evidence, and caches intermediate answer sets. This approach treats the LLM as an agent interacting with the KG to conduct complex, multi-step logical reasoning, thus synergizing both components for enhanced reasoning capabilities.
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