ConStruM improves LLM-based schema matching by using a context tree and global similarity hypergraph to assemble query-specific evidence packs from available schema metadata.
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2026 2verdicts
UNVERDICTED 2representative citing papers
RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.
citing papers explorer
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ConStruM: A Structure-Guided LLM Framework for Context-Aware Schema Matching
ConStruM improves LLM-based schema matching by using a context tree and global similarity hypergraph to assemble query-specific evidence packs from available schema metadata.
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RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.