{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/auto-fuzzyjoin-auto-program-fuzzy-similarity","title":"Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples","arxiv_id":"2103.04489","date":"2021-03-07","proceeding":null,"authors":["Peng Li","Xiang Cheng","Xu Chu","Yeye He","Surajit Chaudhuri"],"abstract":"Fuzzy similarity join is an important database operator widely used in practice. So far the research community has focused exclusively on optimizing fuzzy join \\textit{scalability}. However, practitioners today also struggle to optimize fuzzy-join \\textit{quality}, because they face a daunting space of parameters (e.g., distance-functions, distance-thresholds, tokenization-options, etc.), and often have to resort to a manual trial-and-error approach to program these parameters in order to optimize fuzzy-join quality. This key challenge of automatically generating high-quality fuzzy-join programs has received surprisingly little attention thus far. In this work, we study the problem of \"auto-program\" fuzzy-joins. Leveraging a geometric interpretation of distance-functions, we develop an unsupervised \\textsc{Auto-FuzzyJoin} framework that can infer suitable fuzzy-join programs on given input tables, without requiring explicit human input such as labeled training data. Using \\textsc{Auto-FuzzyJoin}, users only need to provide two input tables $L$ and $R$, and a desired precision target $\\tau$ (say 0.9). \\textsc{Auto-FuzzyJoin} leverages the fact that one of the input is a reference table to automatically program fuzzy-joins that meet the precision target $\\tau$ in expectation, while maximizing fuzzy-join recall (defined as the number of correctly joined records). Experiments on both existing benchmarks and a new benchmark with 50 fuzzy-join tasks created from Wikipedia data suggest that the proposed \\textsc{Auto-FuzzyJoin} significantly outperforms existing unsupervised approaches, and is surprisingly competitive even against supervised approaches (e.g., Magellan and DeepMatcher) when 50\\% of ground-truth labels are used as training data.","url_abs":"https://arxiv.org/abs/2103.04489v1","url_pdf":"https://arxiv.org/pdf/2103.04489v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"auto-fuzzyjoin-auto-program-fuzzy-similarity","repo_url":"https://github.com/chu-data-lab/automaticfuzzyjoin","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"auto-fuzzyjoin-auto-program-fuzzy-similarity","repo_url":"https://github.com/tigerchen52/pearl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"CC0-1.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.04489","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}