{"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/autoblock-a-hands-off-blocking-framework-for","title":"AutoBlock: A Hands-off Blocking Framework for Entity Matching","arxiv_id":"1912.03417","date":"2019-12-07","proceeding":null,"authors":["Wei Zhang","Hao Wei","Bunyamin Sisman","Xin Luna Dong","Christos Faloutsos","David Page"],"abstract":"Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity. Virtually every entity matching task on large datasets requires blocking, a step that reduces the number of record pairs to be matched. However, most of the traditional blocking methods are learning-free and key-based, and their successes are largely built on laborious human effort in cleaning data and designing blocking keys. In this paper, we propose AutoBlock, a novel hands-off blocking framework for entity matching, based on similarity-preserving representation learning and nearest neighbor search. Our contributions include: (a) Automation: AutoBlock frees users from laborious data cleaning and blocking key tuning. (b) Scalability: AutoBlock has a sub-quadratic total time complexity and can be easily deployed for millions of records. (c) Effectiveness: AutoBlock outperforms a wide range of competitive baselines on multiple large-scale, real-world datasets, especially when datasets are dirty and/or unstructured.","url_abs":"https://arxiv.org/abs/1912.03417v1","url_pdf":"https://arxiv.org/pdf/1912.03417v1.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":"abstracts"},"code_links":[{"paper_slug":"autoblock-a-hands-off-blocking-framework-for","repo_url":"https://github.com/vintasoftware/entity-embed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"blocking","task_name":"Blocking"},{"task_slug":"entity-resolution","task_name":"Entity Resolution"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}