arXiv Artificial Intelligence

JoinGR: Learning to Traverse Join Graphs for Table Retrieval

2 hours ago 02.10.2026 07:00 👁 2 views
JoinGR: Learning to Traverse Join Graphs for Table Retrieval

Quick summary

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 in the question and become identifiable only through their join relationships to already relevant tables. We introduce JOINGR, a join-aware table retrieval method that treats the database join graph as the retrieval space. Columns are represented as graph nodes, while intra-table and foreign-key relationships are repres

Key takeaways

  • 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 in the question and become identifiable only through their join relationships to already relevant tables.
  • We introduce JOINGR, a join-aware table retrieval method that treats the database join graph as the retrieval space.

Why it matters

The importance of “JoinGR: Learning to Traverse Join Graphs for Table Retrieval” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗