让大模型更懂企业知识图谱的结构约束，提升推理准确率。
SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs
- 用图谱模式引导推理路径，避免乱跳
- 在真实企业配置库上性能提升显著
- 无需重训练，适配业务逻辑即有效
基于知识图谱的检索增强生成（KG-RAG）支持自然语言与企业结构化知识的交互，但现有代理式方法在公开基准表现良好，却难以泛化到真实企业知识图谱（KGs）——这些图谱结构密集、以模式驱动且受操作约束。为此，我们提出SCAIR（Schema-Conditioned Agentic Iterative Reasoning），一种无需训练的框架，通过注入模式相关的结构先验，并在多跳推理中强制遵循模式感知的遍历策略，实现结构化规划与可控迭代推理的结合。在基于真实世界配置管理数据库（CMDB）构建的企业导向基准上实验表明，SCAIR显著优于现有KG-RAG方法。关键发现是：可靠的企事业图谱推理不能依赖通用代理设计，必须显式融入目标领域的结构与操作约束。我们证明，通过将代理设计对齐业务逻辑，可在无需昂贵模型重训练的前提下实现显著性能提升。
原文摘要 · Abstract (English)
Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) enables natural language interaction with structured enterprise knowledge, yet existing agentic approaches that perform well on public benchmarks often fail to generalize to real-world enterprise Knowledge Graphs (KGs), which are dense, schema-driven, and operationally constrained. To address these limitations, we propose SCAIR (Schema-Conditioned Agentic Iterative Reasoning), a training-free framework that integrates structured planning with controlled iterative reasoning by injecting schema-conditioned structural priors and enforcing schema-aware traversal during multi-hop reasoning. Experiments on an enterprise-oriented benchmark constructed from a real-world Configuration Management DataBase (CMDB) demonstrate that SCAIR substantially improves performance over existing KG-RAG methods. Crucially, our study highlights that reliable enterprise graph reasoning cannot rely on generic agentic designs; instead, it must explicitly incorporate the target domain's structural and operational constraints into the reasoning process. We demonstrate that by aligning agent design with business logic, substantial performance gains can be achieved without the need for costly model retraining.
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