Relational Task Generation Language: A Declarative Specification Framework for Relational Deep Learning

2026-09-01

AI Summary

为解决手动定义关系深度学习任务耗时且易导致数据泄露的问题，本文提出一种声明式语言RTGL以简化任务定义，并展示了其在重构现有任务和设计新任务中的有效性。

Abstract

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.

Problem

• Relational Deep Learning
• Data Leakage
• Task Definition

Innovation

• Relational Task Generation Language
• Declarative Specification
• Relational Deep Learning
• Data Leakage