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Datasets for Supervised Matching in Clean-Clean Entity Resolution
The repository includes 13 established datasets for evaluating ML- and DL-based matching algorithms: Structured DBLP-ACM Structured DLBLP-Scholar Structured iTunes-Amazon Structured Walmart-Amazon Structured BeerAdvo-RateBeer Struct
File Size620.8 MB
Data TypeDataset
Published2022
Licensecc-by-4.0
Total Views2,461
Total Downloads1,377
The repository includes 13 established datasets for evaluating ML- and DL-based matching algorithms:
- Structured DBLP-ACM
- Structured DLBLP-Scholar
- Structured iTunes-Amazon
- Structured Walmart-Amazon
- Structured BeerAdvo-RateBeer
- Structured Amazon-Google Products
- Strucutred Fodors-Zagats
- Dirty DBLP-ACM
- Dirty DBLP-Scholar
- Dirty iTunes-Amazon
- Dirty Walmart-Amazon
- Textual Abt-Buy
- Textual CompanyA-CompanyB
Additionally, the repository includes five new benchmark datasets that are drawn from the following databases using a principled approach based on DeepBlocker:
- Abt-Buy
- Amazon-Google Products
- DBLP-ACM
- IMDB-TMDB
- IMDB-TVDB
- TMDB-TVDB
- Walmart-Amazon
- DBLP-Google Scholar
The datasets are available in different formats so that they can be processed by the following matching algorithms:
- EMTransformer
- GNEM
- HierMatcher
- Magellan
- ZeroER
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Files are hosted on the source repository. Click download to access the full dataset.
George Papadakis (2022). Datasets for Supervised Matching in Clean-Clean Entity Resolution. https://doi.org/10.5281/zenodo.8164151