arXiv AI
JoinGR: Learning to Traverse Join Graphs for Table Retrieval
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arXiv AI
Sep 1
CORE-T: COherent REtrieval of Tables for Text-to-SQL
arXiv:2601.13111v3 Announce Type: replace-cross Abstract: Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes...
arXiv AI
Sep 18
Efficiently Linking Unstructured Data for Multi-step Reasoning
The paper introduces DASE, a query engine designed to efficiently link unstructured data for multi-step reasoning tasks. DASE combines a multi-step reasoning model, a sparse materialized embedding-similarity join index (SemJI), and a co‑designed execution layer to perform multi‑attribute filtering, multi‑vector search, exact relational joins, and thresholded embedding‑similarity joins. In scientific discovery workloads, DASE outperforms traditional RDBMS, rerank, and vector‑database baselines by 6x to 46x in retrieval speed while maintaining comparable recall, and it serves as a high‑recall prefilter that reduces downstream LLM evaluation cost and improves accuracy on benchmarks such as SemBench E‑Commerce.
arXiv AI
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TabJoinBench: A Benchmark for Joinable Table Discovery
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 downstrea...
arXiv AI
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SemJoin: Semantic Join Optimization
arXiv:2606. 29532v1 Announce Type: cross Abstract: Integrating unstructured data into relational database systems is increasingly important as demand grows for natural language querying and analysis.
arXiv Machine Learning
Jun 9
GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation
arXiv:2603. 24925v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, particularly when information is distributed across multiple sources.
arXiv Machine Learning
Aug 27
MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection
MetaSieve is a metapath selection layer that reduces subgraph size in relational deep learning by pruning uninformative metapaths using SQL join and aggregation statistics. It scores candidate metapath extensions with a lightweight function that favors informative yet lightweight paths, discarding those below a threshold. The method is independent of GNN parameters and, when applied to the RelBench benchmark, consistently cuts per‑epoch training time while preserving or improving accuracy.