{"url":"/sota/entity-resolution-on-wdc-products-50-cc","task":{"name":"Entity Resolution","url":"/task/entity-resolution","note":null},"dataset":{"name":"WDC Products-50%cc-unseen-medium","url":"/dataset/wdc-products-1"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"**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)\r\n\r\nSurveys on entity resolution:\r\n\r\n- [Christophides et al.: End-to-End Entity Resolution for Big Data: A Survey](https://arxiv.org/pdf/1905.06397.pdf), 2020.\r\n\r\n- [Barlaug and Gulla: Neural Networks for Entity Matching: A Survey](https://arxiv.org/pdf/2010.11075.pdf), 2021.\r\n\r\nThe task of entity resolution is closely related to the task of [entity alignment](https://paperswithcode.com/task/entity-alignment) which focuses on matching entities between knowledge bases. The task of [entity linking](https://paperswithcode.com/task/entity-linking) differs from entity resolution as entity linking focuses on identifying entity mentions in free text.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["F1 (%)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1 (%)":"higher"}},"counts":{"rows":4,"rows_with_code":4,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"RoBERTa-base","metrics":{"F1 (%)":"71.14"},"uses_additional_data":false,"paper_date":"2023-01-23","paper":"/paper/wdc-products-a-multi-dimensional-entity","paper_url":"https://arxiv.org/abs/2301.09521v2","paper_title":"WDC Products: A Multi-Dimensional Entity Matching Benchmark","code":"https://github.com/wbsg-uni-mannheim/wdcproducts","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"Ditto","metrics":{"F1 (%)":"70.66"},"uses_additional_data":false,"paper_date":"2023-01-23","paper":"/paper/wdc-products-a-multi-dimensional-entity","paper_url":"https://arxiv.org/abs/2301.09521v2","paper_title":"WDC Products: A Multi-Dimensional Entity Matching Benchmark","code":"https://github.com/wbsg-uni-mannheim/wdcproducts","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"HG","metrics":{"F1 (%)":"68.74"},"uses_additional_data":false,"paper_date":"2023-01-23","paper":"/paper/wdc-products-a-multi-dimensional-entity","paper_url":"https://arxiv.org/abs/2301.09521v2","paper_title":"WDC Products: A Multi-Dimensional Entity Matching Benchmark","code":"https://github.com/wbsg-uni-mannheim/wdcproducts","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"RoBERTa-SupCon","metrics":{"F1 (%)":"57.23"},"uses_additional_data":false,"paper_date":"2023-01-23","paper":"/paper/wdc-products-a-multi-dimensional-entity","paper_url":"https://arxiv.org/abs/2301.09521v2","paper_title":"WDC Products: A Multi-Dimensional Entity Matching Benchmark","code":"https://github.com/wbsg-uni-mannheim/wdcproducts","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}