Entity resolution (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)
Blocking is a crucial step in any entity resolution pipeline because a pair-wise comparison of all records across two data sources is infeasible.
Blocking applies a computationally cheap method to generate a smaller set of candidate record pairs reducing the workload of the matcher.
During matching a more expensive pair-wise matcher generates a final set of matching record pairs.
The Amazon-Google dataset for entity resolution derives from the online retailers Amazon.com and the product search service of Google accessible through the Google Base Data API. The dataset contains 1363 entities from amazon.com and 3226 google products as well as a gold standard (perfect mapping) with 1300 matching record pairs between the two data sources. The common attributes between the two data sources are: product name, product description, manufacturer and price. The dataset was initially published in the repository of the Database Group of the University of Leipzig: https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution To enable the reproducibility of the results and the comparability of the performance of different matchers on the Amazon-Google matching task, the dataset was split into fixed train, validation and test sets. The fixed splits are provided in the CompERBench repository: http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html
The Abt-Buy dataset for entity resolution derives from the online retailers Abt.com and Buy.com. The dataset contains 1081 entities from abt.com and 1092 entities from buy.com as well as a gold standard (perfect mapping) with 1097 matching record pairs between the two data sources. The common attributes between the two data sources are: product name, product description and product price. The dataset was initially published in the repository of the Database Group of the University of Leipzig: https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution To enable the reproducibility of the results and the comparability of the performance of different matchers on the Abt-Buy matching task, the dataset was split into fixed train, validation and test sets. The fixed splits are provided in the CompERBench repository: http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html
WDC Block is a benchmark for comparing the performance of blocking methods that are used as part of entity resolution pipelines. Entity resolution aims to identify records in two datasets (A and B) that describe the same real-world entity. Since comparing all record pairs between two datasets can be computationally expensive, entity resolution is approached in two steps, blocking and matching. Blocking applies a computationally cheap method to remove non-matching record pairs and produces a smaller set of candidate record pairs reducing the workload of the matcher. During matching a more expensive pair-wise matcher produces a final set of matching record pairs. Existing benchmark datasets for blocking and matching are rather small with respect to the Cartesian product AxB for comparing all records and the vocabulary size. If blockers are evaluated only on these small datasets, effects resulting from a high number of records or from a large vocabulary size (large number of unique tokens that need to be indexed) may be missed. The Web Data Commons Block (WDC-Block) is a new blocking benchmark that provides much larger datasets and thus requires blockers that address these scalability challenges. WDC Block features a maximal Cartesian product of 200 billion pairs of product offers which were extracted form 3,259 e-shops. Additionally, we provide three development sets with different sizes (~1K pairs, ~5K pairs & ~20K pairs) to experiment with different amounts of training data for the blockers.