BDIViz is a visual analytics system that uses an ensemble of matching algorithms plus LLM validation and interactive heatmaps to improve accuracy and reduce time in biomedical schema matching.
Procopiuc, and Divesh Srivastava
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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.
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BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
BDIViz is a visual analytics system that uses an ensemble of matching algorithms plus LLM validation and interactive heatmaps to improve accuracy and reduce time in biomedical schema matching.
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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.