关系任务生成语言：一种用于关系深度学习的声明式规范框架
Relational Task Generation Language: A Declarative Specification Framework for Relational Deep Learning
- Czech Technical University in Prague（布拉格捷克理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出开源声明式语言RTGL，用于简化RDL任务构建，可重构基准任务、设计新任务，经实验证实其鲁棒性、可用性及与现有RDL框架的无缝集成性。
AI中文摘要：
关系深度学习（RDL）已成为从多表数据中学习的强大范式，但手动定义RDL预测任务是一项繁琐的过程，且常导致数据泄露。为解决该问题，我们引入关系任务生成语言（RTGL）——一种开源声明式语言，通过抽象底层SQL细节简化RDL任务的构建。我们通过重构现有RDL基准任务展示RTGL，发现其源于手动构建的RDL预测目标SQL定义的不一致性，凸显专用声明式语言的价值；此外，我们通过设计多种不同形式和目标类型的新任务，证明RTGL的实用性。实验证实RTGL具有鲁棒性和可用性，可与现有RDL框架无缝集成，便于社区广泛使用。
英文摘要：
Relational Deep Learning (RDL) has become a powerful paradigm for learning from multi-tabular data. However, manually defining RDL prediction tasks is a laborious process that frequently results in data leakage. To address this issue, we introduce Relational Task Generation Language (RTGL) - an open-source declarative language that streamlines RDL task formulation by abstracting away low-level SQL details. We showcase RTGL by reconstructing existing RDL benchmark tasks and uncovering their inconsistencies stemming from manually crafted SQL definitions of RDL prediction targets, thereby underscoring the value of a dedicated declarative language. In addition, we demonstrate the practical utility of RTGL by designing various new tasks with diverse forms and target types. Our experiments confirm the robustness and usability of RTGL, as well as its seamless integration with the existing RDL frameworks, making it widely accessible to the community.
