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SMBench: No-code benchmarking of learning-based entity matching
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DOI：10.1016/j.is.2026.102711.png)
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Abstract
En 中文
Entity Resolution (ER) constitutes a challenging data integration task that is typically addressed through the Filtering–Verification framework. Filtering reduces the quadratic search space in an unsupervised manner that relies on heuristics, whereas verification performs matching, usually through a machine or a deep learning-based approach. Numerous solutions have been proposed for each step, but analyzing their combined performance constitutes a non-trivial task, due to technical and methodological challenges, while the literature typically examines them as orthogonal tasks. We facilitate the benchmarking of state-of-the-art verification algorithms under realistic settings, applying them to the candidate pairs generated by established filtering approaches from popular real-world datasets. To democratize this benchmarking, we developed an open-source, hands-off Web application, called SMBench, which allows users to perform a wealth of experiments through an intuitive user interface that requires no coding or ER expertise. SMBench is publicly available at https://smbench.kbs.uni-hannover.de , while its code is released through https://github.com/erbench/erbench . We delve into its frontend and backend, elaborating on the technologies used for their implementation as well as on the state-of-the-art ER methods they support. Using SMBench, we perform an extended experimental analysis that combines 3 filtering methods with 7 verification approaches, applying them to 9 datasets. The experimental results lead to interesting insights into the relative effectiveness, time and memory efficiency of the considered methods.
Keywords:
Data integration
Entity Resolution
Filtering–verification framework
Supervised matching algorithms
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