用知识图谱提升工业设备运维大模型的推理准确率。
Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations
- 用类型化知识图谱作为数据底座，让LLM只负责生成查询或补全信息。
- 在88个失败场景中，新方法将回答率从0%提升至81.8%。
- 适合需要高可靠性和可审计性的工业AI系统开发者。
基于LLM的工业资产运维代理在平坦文档库上推理时准确率有限。AssetOpsBench（KDD 2026）显示，GPT-4代理在139个工业维护场景中仅达65%准确率，并对比了不同LLM编排范式。本文提出核心问题：工具背后的数据模型有多重要？将类型化知识图谱作为基础底座，按问题类型分路处理：(i) LLM生成Cypher进行结构化检索，使同一GPT-4模型准确率从65%提升至82–83%；(ii) 原生图计算与优化原语，无需LLM，在可图解答场景中达到99%准确率；(iii) 生成增强知识（GAK），对数据缺失部分，代理以溯源标记节点形式生成事实并作答。核心理念为反向使用LLM：限制其仅生成查询或一次补全，由图执行确定性推理。在资产运维基准中标识出的88个非确定性失败场景（涉及10类设备缺失于图中），GAK将设备可回答率从0%提升至100%，覆盖81.8%场景，所有新增事实均标注来源:LLM-derived，支持审计。同时贡献40个图原生场景。结果表明，对结构化操作领域，数据层而非LLM编排是关键杠杆，类型化知识图谱是连接原始工业数据与大模型推理的可靠基底。
原文摘要 · Abstract (English)
LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores. AssetOpsBench (KDD 2026) establishes that GPT-4 agents achieve 65% on 139 industrial maintenance scenarios, and compares LLM orchestration paradigms (Agent-As-Tool vs. Plan-Execute) on a fixed data layer. We ask the orthogonal question: how much does the data model behind the tools matter? We treat a typed knowledge graph as a grounding substrate and route each question by how it is best answered: (i) LLM-generated Cypher for structured retrieval, which lifts the same GPT-4 model from 65% to 82-83%; (ii) native graph and optimization primitives, with no LLM, reaching 99% on graph-answerable scenarios; and (iii) generation-augmented knowledge (GAK) for answers absent from the data -- the engine's agent materializes the missing facts as provenance-tagged graph nodes, then answers. A recurring theme is inverted LLM usage: we constrain the LLM to query generation or one-shot enrichment from a typed schema and let the graph execute deterministically. On the 88 real AssetOpsBench failure-mode scenarios the benchmark itself flags non-deterministic -- ten equipment types absent from the graph -- GAK lifts answerability from zero to 100% of equipment types and answers 81.8% of scenarios, every materialized fact tagged source:LLM-derived for auditability. We also contribute 40 graph-native scenarios. For structured operational domains the data layer -- not the LLM orchestration -- is the primary lever, and a typed knowledge graph serves as a grounding substrate between raw industrial data and LLM reasoning.
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