BDIViz in Action: Interactive Curation and Benchmarking for Schema Matching Methods

• URL: http://arxiv.org/abs/2604.10763v1
• Date: Sun, 12 Apr 2026 18:16:30 GMT
• Title: BDIViz in Action: Interactive Curation and Benchmarking for Schema Matching Methods
• Abstract summary: BDIViz is an interactive visualization system for schema matching with LLM-assisted validation.<n>New extension to BDIViz addresses a critical need in data integration research: human-in-the-loop benchmarking and iterative matcher development.
• Score: 12.444984647326024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Schema matching remains fundamental to data integration, yet evaluating and comparing matching methods is hindered by limited benchmark diversity and lack of interactive validation frameworks. BDIViz, recently published at IEEE VIS 2025, is an interactive visualization system for schema matching with LLM-assisted validation. Given source and target datasets, BDIViz applies automatic matching methods and visualizes candidates in an interactive heatmap with hierarchical navigation, zoom, and filtering. Users validate matches directly in the heatmap and inspect ambiguous cases using coordinated views that show attribute descriptions, example values, and distributions. An LLM assistant generates structured explanations for selected candidates to support decision-making. This demonstration showcases a new extension to BDIViz that addresses a critical need in data integration research: human-in-the-loop benchmarking and iterative matcher development. New matchers can be integrated through a standardized interface, while user validations become evolving ground truth for real-time performance evaluation. This enables benchmarking new algorithms, constructing high-quality ground-truth datasets through expert validation, and comparing matcher behavior across diverse schemas and domains. We demonstrate two complementary scenarios: (i) data harmonization, where users map a large tabular dataset to a target schema with value-level inspection and LLM-generated explanations; and (ii) developer-in-the-loop benchmarking, where developers integrate custom matchers, observe performance metrics, and refine their algorithms.

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