构建企业级多表关联数据集，助力表格表示学习
SALT: Sales Autocompletion Linked Business Tables Dataset
- 基于ERP系统构建多表关联数据集，真实还原企业业务场景
- 涵盖大量通过外键链接的结构化表格，支持复杂查询与推理
- 适合研究表格嵌入、跨表对齐及企业级大模型应用的学者
基础模型，尤其是采用Transformer架构的模型，在自然语言处理和图像处理等领域表现出色。然而，将这些模型应用于表格等结构化数据时面临重大挑战，尤其是在处理通过外键关联的多表数据时，这类数据在企业环境中普遍存在，对支撑业务应用场景至关重要。尽管影响深远，针对企业环境下此类关联业务表格的研究仍是一个重要但未被充分探索的领域。为此，我们从企业资源规划（ERP）系统中收集并构建了一个包含大量关联表格的精选数据集。该数据集专为支持表格表示学习的研究而设计，通过提供真实的企业级数据，旨在提升模型在实际商业环境中的有效性和适用性。
原文摘要 · Abstract (English)
Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain. To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.
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