研究人员提出了两种新的数据库表检索方法，以应对诸如 Text-to-SQL 等任务中查找相关表的挑战。TabJoinBench 提供了一个标准化的基准测试，用于评估连接发现方法，旨在提高不同技术之间的可复现性和公平比较。另一方面，JoinGR 是一种新颖的方法，它利用数据库的连接图来识别表，即使是那些在查询中未明确提及的表，通过遍历表之间的关系来实现。 AI
影响 表检索的进步可以提高与结构化数据交互的人工智能系统的准确性和效率，例如 Text-to-SQL 模型。
排序理由 两篇研究论文介绍了数据库中表检索的新基准测试和方法。
在 arXiv cs.IR (Information Retrieval) 阅读 →
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arXiv:2610.00817v1 Announce Type: cross Abstract: Join discovery aims to identify tables from large data repositories that can augment a query table with complementary information, enabling downstream tasks such as data exploration, feature engineering, and business intelligence.…
arXiv:2610.01064v1 Announce Type: cross Abstract: Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases. Dense table retrievers rank schema elements independently, but this ignores a key source of evidence: some required tables are not mentioned i…
Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases. Dense table retrievers rank schema elements independently, but this ignores a key source of evidence: some required tables are not mentioned in the question and become identifiable only throug…
Join discovery aims to identify tables from large data repositories that can augment a query table with complementary information, enabling downstream tasks such as data exploration, feature engineering, and business intelligence. Although numerous join discovery methods have bee…