AgentOnto：用本体引导 LLM 智能体做企业数据分析
AgentOnto: Ontology-Guided LLM Agents for Enterprise Data Analytics
一篇 arXiv 论文，讲怎么把企业里散落的数据库和文档整理成本体，让 LLM 智能体做数据分析更靠谱，还能吸收反馈，有真实落地案例。
AgentOnto 是一个面向企业数据分析的本体引导式 LLM 智能体，将数据库、业务文档和既有示例任务中的知识组织为可指导分析的本体。其 Iterative Ontology Preparation 机制通过自生成的分析任务迭代改进本体，并能吸收已确认的反馈。在两个公开数据源的评测中，AgentOnto 明显优于代表性方法，模块级评测验证了本体构建、分析与更新各环节的有效性。论文还在真实企业环境中部署验证了其实用价值。
AgentOnto: Ontology-Guided LLM Agents for Enterprise Data Analytics
Enterprise data analysis requires agents to connect analytical requests with knowledge distributed across databases, business documents, and existing example tasks. Without an explicit representation of this knowledge, an agent may produce incorrect analytical results and cannot reliably incorporate feedback. We present AgentOnto, an ontology-guided LLM agent for enterprise data analytics. AgentOnto organizes heterogeneous enterprise knowledge into an ontology that directly guides analysis and incorporates confirmed feedback. Iterative Ontology Preparation further improves the ontology through self-generated analysis tasks. We evaluate AgentOnto on two public data sources. AgentOnto substantially outperforms representative methods, while module-level evaluations confirm the effectiveness of ontology construction, analysis, and updating. A real-world deployment further demonstrates its practical value in enterprise analytics. These results demonstrate that explicitly constructing, using, and updating enterprise knowledge supports reliable and adaptable data analysis.
