用大模型+图数据库实现企业通信精准触达，结果可解释。
An Explainable Natural Language Framework for Identifying and Notifying Target Audiences In Enterprise Communication
- 结合知识图谱与大模型，理解自然语言查询
- 支持设备、厂商、工程师等多维度精准定位受众
- 适合需要透明决策过程的企业沟通场景
在大规模运维组织中，识别领域专家并管理复杂实体间的关系面临巨大挑战——传统沟通方式难以应对信息过载和响应延迟问题。本文提出一种新框架，将RDF图数据库与大模型结合，处理自然语言查询以实现精准受众定位，并通过规划-编排架构提供透明推理过程。该方案使沟通发起者能够使用设备、制造商、维护工程师、设施等概念组合成直观查询，输出可解释的结果，既保持系统可信度，又提升组织内沟通效率。
原文摘要 · Abstract (English)
In large-scale maintenance organizations, identifying subject matter experts and managing communications across complex entities relationships poses significant challenges -- including information overload and longer response times -- that traditional communication approaches fail to address effectively. We propose a novel framework that combines RDF graph databases with LLMs to process natural language queries for precise audience targeting, while providing transparent reasoning through a planning-orchestration architecture. Our solution enables communication owners to formulate intuitive queries combining concepts such as equipment, manufacturers, maintenance engineers, and facilities, delivering explainable results that maintain trust in the system while improving communication efficiency across the organization.
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