{"url":"/dataset/amazon-google","name":"Amazon-Google","full_name":null,"description_markdown":"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.\r\n\r\nThe 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](https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution)\r\n\r\nTo 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:\r\n\r\n[http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html](http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html)","description_withheld":null,"homepage":"https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution","introduced_date":"2010-09-01","introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons license","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"Data Integration","url":"/task/data-integration","datasets_with_task":"/datasets/task/data-integration"},{"name":"Entity Resolution","url":"/task/entity-resolution","datasets_with_task":"/datasets/task/entity-resolution"},{"name":"Blocking","url":"/task/blocking","datasets_with_task":"/datasets/task/blocking"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Amazon-Google"],"data_loaders":[],"num_papers_in_archive":20,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/entity-resolution-on-amazon-google","task":"Entity Resolution","dataset_variant":"Amazon-Google","rows":17,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"gpt4-0613_fewshot-10","paper":"/paper/entity-matching-using-large-language-models","metrics":{"F1 (%)":"85.21"},"code_links":[{"title":"wbsg-uni-mannheim/matchgpt","url":"https://github.com/wbsg-uni-mannheim/matchgpt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/blocking-on-amazon-google","task":"Blocking","dataset_variant":"Amazon-Google","rows":6,"metrics":["Candidate Set Size","Recall"],"first_row_in_archive_order":{"model":"SC-Block","paper":"/paper/sc-block-supervised-contrastive-blocking","metrics":{"Candidate Set Size":"11000","Recall":"99.6"},"code_links":[{"title":"wbsg-uni-mannheim/sc-block","url":"https://github.com/wbsg-uni-mannheim/sc-block"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fine-tuning-large-language-models-for-entity","title":"Fine-tuning Large Language Models for Entity Matching","date":"2024-09-12","rows_on_this_dataset":6,"code_links":1,"syntology":null},{"paper":"/paper/entity-matching-using-large-language-models","title":"Entity Matching using Large Language Models","date":"2023-10-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sparkly-a-simple-yet-surprisingly-strong-tf","title":"Sparkly: A Simple yet Surprisingly Strong TF/IDF Blocker for Entity Matching","date":"2023-04-20","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/sc-block-supervised-contrastive-blocking","title":"SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines","date":"2023-03-06","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/deduplication-over-heterogeneous-attribute","title":"Deduplication Over Heterogeneous Attribute Types (D-HAT)","date":"2022-11-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/probing-the-robustness-of-pre-trained","title":"Probing the Robustness of Pre-trained Language Models for Entity Matching","date":"2022-10-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sudowoodo-contrastive-self-supervised","title":"Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation","date":"2022-07-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/entity-resolution-with-hierarchical-graph","title":"Entity Resolution with Hierarchical Graph Attention Networks","date":"2022-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/can-foundation-models-wrangle-your-data","title":"Can Foundation Models Wrangle Your Data?","date":"2022-05-20","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/supervised-contrastive-learning-for-product","title":"Supervised Contrastive Learning for Product Matching","date":"2022-02-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-learning-for-blocking-in-entity-matching","title":"Deep learning for blocking in entity matching: a design space exploration","date":"2021-07-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/profiling-entity-matching-benchmark-tasks","title":"Profiling Entity Matching Benchmark Tasks","date":"2020-10-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cordel-a-contrastive-deep-learning-approach","title":"CorDEL: A Contrastive Deep Learning Approach for Entity Linkage","date":"2020-09-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-entity-matching-with-pre-trained","title":"Deep Entity Matching with Pre-Trained Language Models","date":"2020-04-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-learning-for-entity-matching-a-design","title":"Deep Learning for Entity Matching: A Design Space Exploration","date":"2018-05-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}