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Leveraging Knowledge Graphs and LLM Reasoning to Identify ... arxiv.org 5 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.
referenceThe experimental evaluation of the LLM agent framework utilized OpenAI’s GPT-4o via Langchain QA chains, interacting with a Neo4j knowledge graph through LLM-generated Cypher queries, with configuration settings of temperature 0.0, top_p 0.95, and a 4096-token limit.
procedureThe operational query process employs a QA chain guided by a step-wise approach that decomposes input questions into structured steps, where each step involves Cypher generation, knowledge graph querying, and self-reflection.
procedureFor each sub-question, the framework generates Cypher queries for Knowledge Graph interaction, extracts information, and performs self-reflection to identify and correct potential errors.
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.
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org 2 facts
procedureThe CoE exploration process involves a cyclical lookup phase consisting of four steps: (1) executing Cypher queries to retrieve connected nodes and relationships in the knowledge graph, (2) ranking nodes or relationships by relevance using dense vector embeddings, (3) utilizing an LLM to filter and select the most relevant nodes or relationships for continuing exploration hops, and (4) evaluating the alignment of the current traversal with the initial plan to decide whether to continue, adjust, or synthesize a response.
procedureThe CoE exploration process involves a cyclical lookup phase consisting of four steps: (1) executing Cypher queries to retrieve connected nodes and relationships in the knowledge graph, (2) ranking nodes or relationships by relevance using dense vector embeddings, (3) utilizing an LLM to filter and select the most relevant nodes or relationships for continuing exploration hops, and (4) evaluating the alignment of the current traversal with the initial plan to decide whether to continue, adjust, or synthesize a response.
Enhancing LLMs with Knowledge Graphs: A Case Study - LinkedIn linkedin.com 1 fact
procedureTo fact-check the LLM, the authors use the Cypher query language to return relevant coverage nodes and their descriptions from the knowledge graph, then perform a similarity match between the LLM response and the retrieved knowledge graph information using embeddings.