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ZENODO
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Datasets for Supervised Matching in Clean-Clean Entity Resolution

Authors: George Papadakis;

Datasets for Supervised Matching in Clean-Clean Entity Resolution

Abstract

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

Keywords

DL-based Mathing, ML-based Matching, Entity Resolution

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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1
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31