EvoOntology：面向数据智能体的自进化本体层
EvoOntology: A Self-Evolving Ontology Layer for Data Agents
- Renmin University of China（中国人民大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
EvoOntology提出自进化本体层，通过MCP服务器封装模式、内容和工具层，让数据智能体运行时主动查询交互，经归因引导编辑与配对评估持续优化，在三个基准上优于现有方法。
AI中文摘要：
数据智能体旨在对异构数据（包括表格、文件和数据库）执行自然语言指令。然而，数据智能体面临一个具有挑战性的智能体-数据鸿沟：异构数据位于智能体外部，而智能体只能通过通用工具（例如列名和文件路径）访问这些数据。现有方法要么让智能体直接探索原始数据源，要么将手动构建的语义层注入提示中。然而，这两种方法都无法很好地扩展到大型异构数据源，也无法适应不同的智能体行为。在本文中，我们介绍了EvoOntology，一个用于数据智能体的自进化本体层。EvoOntology将本体封装为一个MCP服务器，该服务器包含模式层、内容层和工具层，使智能体能够在运行时主动查询本体并与之交互。为此，我们引入了一个用于自主本体构建的构建智能体，以及一个自进化循环，该循环通过基于归因引导的类型化编辑持续优化本体，这些编辑仅在骨干条件配对评估通过后才被接受。在三个广泛采用的数据智能体基准测试中，使用四种LLM骨干模型的实验表明，EvoOntology始终优于强基线和现有语义层方法，有效弥合了智能体-数据鸿沟，并实现了与异构数据的更有效交互。代码：此https URL
英文摘要：
Data agents aim to fulfill natural-language instructions over heterogeneous data, including tables, files, and databases. However, data agents face a challenging agent-data gap: heterogeneous data resides outside the agent, while the agent can access it (e.g., column names and file paths) only through generic tools. Existing approaches either let agents directly explore raw data sources or inject manually constructed semantic layers into prompts. However, neither scales well to large heterogeneous data sources nor adapts to different agent behaviors. In this paper, we introduce EvoOntology, a self-evolving ontology layer for data agents. EvoOntology encapsulates the ontology as an MCP server comprising a schema layer, a content layer, and a tool layer, enabling agents to actively query and interact with the ontology at runtime. To this end, we introduce a builder agent for autonomous ontology construction and a self-evolution loop that continuously refines the ontology through attribution-guided typed edits that are accepted only after a backbone-conditional paired evaluation. Experiments on three well-adopted data-agent benchmarks with four LLM backbones demonstrate that EvoOntology consistently outperforms strong baselines and existing semantic-layer approaches, effectively bridging the agent-data gap and enabling more effective interaction with heterogeneous data. Code: https://github.com/ruc-datalab/EvoOntology
