标题： 基于信息理论原则的关系数据预训练框架 显示英文标题

标题： A Pre-training Framework for Relational Data with Information-theoretic Principles

摘要： 关系型数据库支撑着多个领域中的关键基础设施，但由于任务异质性，设计可泛化的预训练策略以从关系型数据库中学习仍然是一个开放挑战。 具体而言，存在无限多的可能下游任务，因为任务是基于关系模式图、时间依赖性和SQL定义的标签逻辑来定义的。 需要一种有效的预训练框架，以考虑这些因素，从而获得任务感知的表示。 通过结合驱动标签生成的基础分布的知识，下游任务可以受益于相关的侧信道信息。 为弥合这一差距，我们引入了任务向量估计（TVE），这是一种新颖的预训练框架，通过在模式遍历图上的集合聚合构建预测监督信号，显式建模下一个窗口的关系动态。 我们通过信息论的角度形式化了我们的方法，证明了任务知情表示比没有任务先验获得的表示保留了更多的相关信号。 在RelBench基准上的大量实验表明，TVE始终优于传统的预训练基线。 我们的研究结果提倡将任务异质性和时间结构作为设计原则，纳入关系型数据库上预测建模的预训练目标。 显示英文摘要

摘要： Relational databases underpin critical infrastructure across a wide range of domains, yet the design of generalizable pre-training strategies for learning from relational databases remains an open challenge due to task heterogeneity. Specifically, there exist infinitely many possible downstream tasks, as tasks are defined based on relational schema graphs, temporal dependencies, and SQL-defined label logics. An effective pre-training framework is desired to take these factors into account in order to obtain task-aware representations. By incorporating knowledge of the underlying distribution that drives label generation, downstream tasks can benefit from relevant side-channel information. To bridge this gap, we introduce Task Vector Estimation (TVE), a novel pre-training framework that constructs predictive supervisory signals via set-based aggregation over schema traversal graphs, explicitly modeling next-window relational dynamics. We formalize our approach through an information-theoretic lens, demonstrating that task-informed representations retain more relevant signals than those obtained without task priors. Extensive experiments on the RelBench benchmark show that TVE consistently outperforms traditional pre-training baselines. Our findings advocate for pre-training objectives that encode task heterogeneity and temporal structure as design principles for predictive modeling on relational databases.