一种面向自主上下文感知数据质量评估的智能体检索框架
An Agentic Retrieval Framework for Autonomous Context-Aware Data Quality Assessment
AI总结：
提出一种智能体检索框架，通过多智能体工作流理解数据使用意图、推导上下文感知评估策略并生成可执行验证逻辑，引入可行性验证阶段确保可靠性，实验表明能自适应不同使用场景并减少不可执行规则。
摘要：
数据质量评估是有效数据分析和数据驱动决策的关键前提，但由于数据质量固有的上下文依赖性，它仍然是一项具有挑战性的任务。现有方法通常依赖静态规则或手动评估策略，限制了它们对不同使用场景的适应性，并制约了大规模自动化。人工智能的最新进展，特别是大语言模型，为自动化数据质量评估提供了新机遇，但也引发了与可靠性、基础性和执行安全性相关的担忧。在本文中，我们提出了一种统一的智能体检索框架，用于自主上下文感知数据质量评估。该框架解释预期数据使用的自然语言描述，推导上下文感知评估策略，并通过多智能体工作流生成可执行验证逻辑。为确保操作可靠性，该框架引入了一个可行性验证阶段，在执行前评估生成的评估规范的真实性和可执行性，从而在必要时进行迭代改进。接受的验证逻辑被确定性地执行，以保证可重复和可审计的结果。我们将所提出的框架实现为一个端到端原型，并在同一数据集上的多个使用场景中进行了评估。结果表明，评估结果能够有意义地适应不同的预期用途，而可行性门控执行减少了不切实际或不可执行规则的生成。所提出的方法为在现代数据驱动环境中部署自主且可控的数据质量评估提供了实用基础。
英文摘要：
Data quality assessment is a critical prerequisite for effective data analytics and data-driven decision-making, yet it remains a challenging task due to the inherently context-dependent nature of data quality. Existing approaches often rely on static rules or manual assessment strategies, limiting their adaptability to diverse usage scenarios and constraining automation at scale. Recent advances in artificial intelligence, particularly large language models, offer new opportunities for automating data quality assessment, but raise concerns related to reliability, grounding, and execution safety. In this paper, we propose a unified agentic-retrieval framework for autonomous context-aware data quality assessment. The framework interprets natural-language descriptions of intended data usage, derives context-aware assessment strategies, and generates executable validation logic through a multi-agent workflow. To ensure operational reliability, the framework introduces a feasibility validation stage that evaluates the realism and executability of generated assessment specifications before execution, enabling iterative refinement when necessary. Accepted validation logic is executed deterministically to guarantee reproducible and auditable results. We implement the proposed framework as an end-to-end prototype and evaluate it across multiple usage scenarios applied to the same dataset. The results demonstrate that assessment outcomes adapt meaningfully to different intended uses, while feasibility-gated execution reduces unrealistic or non-executable rule generation. The proposed approach provides a practical foundation for deploying autonomous yet controlled data quality assessment in modern data-driven environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
