{"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/deep-learning-for-entity-matching-a-design","title":"Deep Learning for Entity Matching: A Design Space Exploration","arxiv_id":null,"date":"2018-05-01","proceeding":"SIGMOD: International Conference on Management of Data 2018 5","authors":["Sidharth Mudgal","Han Li","Theodoros Rekatsinas","AnHai Doan","Youngchoon Park","Ganesh Krishnan","Rohit Deep","Esteban Arcaute","Vijay Raghavendra"],"abstract":"Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that have been developed for related matching tasks in text processing (e.g., entity linking, textual entailment, etc.). We categorize these solutions and define a space of DL solutions for EM, as embodied by four solutions with varying representational power: SIF, RNN, Attention, and Hybrid. Next, we investigate the types of EM problems for which DL can be helpful. We consider three such problem types, which match structured data instances, textual instances, and dirty instances, respectively. We empirically compare the above four DL solutions with Magellan, a state-of-the-art learning-based EM solution. The results show that DL does not outperform current solutions on structured EM, but it can significantly outperform them on textual and dirty EM. For practitioners, this suggests that they should seriously consider using DL for textual and dirty EM problems. Finally, we analyze DL's performance and discuss future research directions.","url_abs":"https://doi.org/10.1145/3183713.3196926","url_pdf":"http://pages.cs.wisc.edu/~anhai/papers1/deepmatcher-sigmod18.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":"deep-learning-for-entity-matching-a-design","repo_url":"https://github.com/anhaidgroup/deepmatcher","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"entity-resolution","task_name":"Entity Resolution"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/entity-resolution-on-abt-buy","task":"Entity Resolution","dataset":"Abt-Buy","model":"DeepMatcher - Hybrid","rank_in_archive_order":14,"of":16,"metrics":{"F1 (%)":"62.80"},"uses_additional_data":false},{"leaderboard":"/sota/entity-resolution-on-amazon-google","task":"Entity Resolution","dataset":"Amazon-Google","model":"DeepMatcher - Hybrid","rank_in_archive_order":9,"of":17,"metrics":{"F1 (%)":"69.3"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}