@inproceedings{chaturvedi-etal-2026-scair,
    title = "{SCAIR}: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs",
    author = "Chaturvedi, Prateek  and
      Zhu, Yuqicheng  and
      Zhou, Hongkuan  and
      Zhou, Dongzhuoran  and
      He, Yunjie  and
      Staab, Steffen  and
      Du, Fei  and
      Tang, Jie  and
      Kharlamov, Evgeny",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.76/",
    pages = "1089--1104",
    ISBN = "979-8-89176-394-4",
    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."
}