arxiv:2609.15779
Published on Sep 14
· Submitted by
Shaolei Zhang on Sep 21
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Abstract
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
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Paper submitter 2 days ago
EvoOntology: the First Self-Evolving Ontology Layer for Data Agents
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🔌 Universal Agent Plugin as MCP: Seamlessly integrates with Claude Code, Codex, and other AI agents.
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🧠 Automatic Ontology Construction: Builds a tailored ontology layer directly from your data.
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♻️ Continuous Self-Evolution: Continuously evolves and refines the ontology layer based on interaction history.
1 day ago
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