给AI配个会自我更新的数据地图
数据智能体要按人话去查表格、文件、数据库，但它看到的只是列名和路径，像隔着墙摸东西。这篇给它加了一层会自己长大的「本体层」:一个AI代理自动把数据源的结构、内容和查询工具整理成地图，每次用完后根据效果自我修正，改对了才保留。在三个主流评测集、四种大模型上，加了这层的智能体都稳定超过现有方案。它不是你明天能装上的插件，但指向一个趋势：让AI自己维护对数据的理解，而不是靠人手工喂语义。
📄 原文摘要(英文)
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