Knowledge Tree
Relations (1)
related 2.81 — strongly supporting 6 facts
LLM-based agents utilize Knowledge Graphs as a structured data source to perform complex reasoning, query processing, and information synthesis as described in [1], [2], and [3]. Furthermore, frameworks like PoG integrate these agents with Knowledge Graphs to enable adaptive exploration and iterative reasoning over graph-based data [4] and [5].
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Leveraging Knowledge Graphs and LLM Reasoning to Identify ... arxiv.org 4 facts
procedureThe LLM agent's query processing procedure follows these steps: (1) The agent receives a complex natural language query regarding warehouse performance or planning. (2) The agent autonomously generates a sequence of sub-questions, formulated one at a time and conditioned on evidence from previous sub-question answers. (3) For each sub-question, the agent generates a precise NL-to-Graph Cypher query for Knowledge Graph interaction, as referenced in Hornsteiner et al. (2024) and Mandilara et al. (2025). (4) The agent retrieves relevant information. (5) The agent performs self-reflection, as referenced in Huang et al. (2022) and Madaan et al. (2023), to validate findings and correct errors in the analytical pathway.
procedureThe Summarizer module in the proposed LLM agent framework performs final answer synthesis by interpreting aggregated knowledge graph data to identify performance bottlenecks and suggest causal factors by traversing relationships within the graph.
procedureThe research framework aims to enable LLM-based agents to transform natural language questions about Discrete Event Simulation (DES) output into executable queries over a Knowledge Graph, iteratively refine analytical paths based on retrieved evidence, and synthesize information from disparate parts of the Knowledge Graph to diagnose operational issues.
referenceThe proposed framework for warehouse operational analysis consists of two main components: the ontological construction of a Knowledge Graph from Discrete Event Simulation output data, and an LLM-agent equipped with an iterative reasoning mechanism that features sequential sub-questioning, Cypher generation for Knowledge Graph interaction, and self-reflection.
How to Improve Multi-Hop Reasoning With Knowledge Graphs and ... neo4j.com 1 fact
procedureAn LLM agent using a chain-of-thought flow to answer a question about the founders of Prosper Robotics follows this procedure: (1) separates the query into sub-questions ('Who is the founder of Prosper Robotics?' and 'What’s the latest news about the founder?'), (2) queries a knowledge graph to identify the founder as Shariq Hashme, and (3) rewrites the second question to 'What’s the latest news about Shariq Hashme?' to retrieve the final answer.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org 1 fact
claimPoG (Chen et al., 2024a) integrates reflection and self-correction mechanisms to adaptively explore reasoning paths over a knowledge graph via an LLM agent, augmenting the LLM in complex reasoning and question answering.