SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs

AI 中文解读

SCAIR给企业知识图谱装上“业务大脑”，不用重新训练模型，靠注入业务规则就能让AI推理准确率大幅提升。传统AI在处理企业级知识图谱时总水土不服——公共测试里的好成绩在真实业务场景中经常翻车。原因很简单：企业数据不仅有庞杂的关系网，还带着严格的业务规则和操作限制。SCAIR就像给推理过程配了一本“业务手册”，让AI在一步步查找答案时，始终按照企业的真实逻辑走，而不是凭通用常识瞎猜。最关键的是，这个方案完全不需要昂贵的模型重新训练，靠巧妙的设计就能见效。对企业员工来说，最直接的改变是能像聊天一样向系统提问。比如问“上个月哪些服务器配置变更可能引发风险”，AI会严格按照企业运维规则和数据库结构，准确给出答案，而不是给出似是而非的泛泛建议。这意味着IT运维、财务分析等岗位的同事，可以更放心地用自然语言与公司核心数据交互，减少误判，大幅提升工作效率。

arXiv:2607.22571v1 Announce Type: new Abstract: 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.