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The paper introduces ColRel, a two-stage approach that uses large language models to discover relationships between columns in data lakes. It first creates column embeddings from available metadata and data at ingestion, then refines these embeddings with business dictionaries to generate concise natural-language descriptions. Experiments on public benchmarks and an industrial ERP dataset demonstrate ColRel’s effectiveness, especially in scenarios with weak signals and semantically related columns.
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...
SciNLP is a new benchmark dataset for full‑text entity and relation extraction in the NLP domain, comprising 60 manually annotated papers with 6,429 entities and 1,649 relations. It is the first dataset to provide full‑text annotations of entities and their relationships specifically for NLP literature. Experiments show that models trained on SciNLP outperform baselines on certain tasks, and the dataset enabled the automatic construction of a fine‑grained knowledge graph with an average node degree of 3.3.
arXiv:2606. 28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.
arXiv:2609.26658v1 Announce Type: cross Abstract: Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit att...
Retrieval in the SQL setting has largely been studied as the task of finding, within a large collection of SQL statements, the statement that answers a natural-language question. At scale, however, a more fundamental retrieval problem precedes generation: schema retrieval, identifying the tables and columns a question requires in a database that may contain thousands of them, far more than fit in a model's context.