{"url":"/dataset/abt-buy","name":"Abt-Buy","full_name":null,"description_markdown":"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. \r\n\r\nThe dataset was initially published in the repository of the Database Group of the University of Leipzig:\r\n[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 Abt-Buy matching task, the dataset was split into fixed train, validation and test sets. \r\nThe 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":["Abt-Buy"],"data_loaders":[],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/entity-resolution-on-abt-buy","task":"Entity Resolution","dataset_variant":"Abt-Buy","rows":16,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"gpt4-0613_zeroshot","paper":"/paper/entity-matching-using-large-language-models","metrics":{"F1 (%)":"95.78"},"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-abt-buy","task":"Blocking","dataset_variant":"Abt-Buy","rows":6,"metrics":["Candidate Set Size","Recall"],"first_row_in_archive_order":{"model":"Sudowoodo","paper":"/paper/sudowoodo-contrastive-self-supervised","metrics":{"Candidate Set Size":"3276","Recall":"88.6"},"code_links":[{"title":"megagonlabs/sudowoodo","url":"https://github.com/megagonlabs/sudowoodo"}]},"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/domain-adaptation-for-deep-entity-resolution","title":"Domain Adaptation for Deep Entity Resolution: A Design Space Exploration","date":"2022-06-01","rows_on_this_dataset":1,"code_links":1,"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/dual-objective-fine-tuning-of-bert-for-entity","title":"Dual-Objective Fine-Tuning of BERT for Entity Matching","date":"2021-06-01","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/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."}