UniDataAgent：一种面向企业问题到报告自动化的本体基础智能体

中文摘要 AI 辅助

企业数据智能体必须保留组织特定的语义，而不仅仅是将问题转化为查询。我们提出了中国联通数据智能体（UniDataAgent），一个基于本体的可复用问题到报告分析系统，该系统将语义获取与在线执行分离。本体获取与验证阶段（OAV）通过专家编写的业务技能、约束生成、问题验证和选择性专家评审，从元数据、业务知识和支持材料中构建版本化的企业本体。问题到报告执行阶段（QRE）为每个问题检索语义契约，协调技能和数据工具，验证结果，并生成带有证据链接的报告。在27个企业表和大约数千种指标类型中，本体构建仅需几个小时，而手动方式大约需要一周。生成报告仅需几分钟，而手动方式需要数个工作日。本体基础在真实业务问题上实现了95.0%的严格准确率，而文档RAG为72.5%，尤其是在结构化和组合任务上。该系统已部署以节省成本，并有可能在其他企业中复制。

英文摘要

Enterprise data agents must preserve organization specific semantics, not just translate questions into queries. We present ChinaUnicom DataAgent (UniDataAgent), an ontology grounded system for reusable question-to-report analysis that separates semantic acquisition from online execution. Ontology Acquisition and Validation stage (OAV) builds versioned enterprise ontologies from metadata, business knowledge, and supporting materials through expert authored business skills, constrained generation, question verification, and selected expert review. Question-to-Report Execution (QRE) stage retrieves semantic contracts for each question, coordinates skills and data tools, validates results, and produces evidence linked reports. Across 27 enterprise tables and roughly thousands of metric types, ontology construction took a few hours instead of about one week manually. It took just a few minutes to generate the reports, instead of several working days. Ontology grounding achieved 95.0\% strict accuracy on real business questions, versus 72.5\% for document RAG, especially on structured and compositional tasks. The system has already been deployed to generate cost savings and has the potential to be replicated in other enterprises.

发表机构

• China Unicom Software Research Institute（中国联通软件研究院）
• College Of Artificial Intelligence, Nankai University（南开大学人工智能学院）

机构由 AI 辅助整理，请以论文原文为准。