{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T01:14:40Z","timestamp":1781918080305,"version":"3.54.5"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>\n            Entity resolution (ER) is a core data integration problem that identifies pairs of data instances referring to the same real-world entities, and the state-of-the-art results of ER are achieved by deep learning (DL) based approaches. However, DL-based approaches typically require a large amount of labeled training data\n            <jats:italic>(i.e.<\/jats:italic>\n            , matching and non-matching pairs), which incurs substantial manual labeling efforts. In this paper, we introduce\n            <jats:bold>DADER<\/jats:bold>\n            , a hands-off deep ER system through domain adaptation.\n            <jats:bold>DADER<\/jats:bold>\n            utilizes multiple well-labeled source ER datasets to train a DL-based ER model for a new target ER dataset that does not have any labels or with only a few labels. To address the key challenge of domain shift,\n            <jats:bold>DADER<\/jats:bold>\n            judiciously selects labeled entity pairs from the source and then aligns distributions of the source and the target by using six popular domain adaptation strategies.\n            <jats:bold>DADER<\/jats:bold>\n            can also harness the users to gather a few labels for further improvement. We have built\n            <jats:bold>DADER<\/jats:bold>\n            as an open-sourced Python Library with intuitive APIs and demonstrated its utility on supporting hands-off ER in real-world scenarios.\n          <\/jats:p>","DOI":"10.14778\/3554821.3554870","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3666-3669","source":"Crossref","is-referenced-by-count":13,"title":["DADER"],"prefix":"10.14778","volume":"15","author":[{"given":"Jianhong","family":"Tu","sequence":"first","affiliation":[{"name":"Renmin University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyue","family":"Han","sequence":"additional","affiliation":[{"name":"Renmin University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju","family":"Fan","sequence":"additional","affiliation":[{"name":"Renmin University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Tang","sequence":"additional","affiliation":[{"name":"QCRI, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengliang","family":"Chai","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyong","family":"Du","sequence":"additional","affiliation":[{"name":"Renmin University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Mikhail Bilenko and Raymond J Mooney. 2003. Adaptive duplicate detection using learnable string similarity measures. In SIGKDD. 39--48.  Mikhail Bilenko and Raymond J Mooney. 2003. Adaptive duplicate detection using learnable string similarity measures. In SIGKDD. 39--48.","DOI":"10.1145\/956750.956759"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3405476"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/3236187.3236198"},{"key":"e_1_2_1_4_1","volume-title":"Deep entity matching with pre-trained language models. arXiv preprint arXiv:2004.00584","author":"Li Yuliang","year":"2020","unstructured":"Yuliang Li , Jinfeng Li , Yoshihiko Suhara , AnHai Doan , and Wang-Chiew Tan . 2020. Deep entity matching with pre-trained language models. arXiv preprint arXiv:2004.00584 ( 2020 ). Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. 2020. Deep entity matching with pre-trained language models. arXiv preprint arXiv:2004.00584 (2020)."},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Sidharth Mudgal Han Li Theodoros Rekatsinas AnHai Doan Youngchoon Park Ganesh Krishnan Rohit Deep Esteban Arcaute and Vijay Raghavendra. 2018. Deep learning for entity matching: A design space exploration. In SIGMOD. 19--34.  Sidharth Mudgal Han Li Theodoros Rekatsinas AnHai Doan Youngchoon Park Ganesh Krishnan Rohit Deep Esteban Arcaute and Vijay Raghavendra. 2018. Deep learning for entity matching: A design space exploration. In SIGMOD. 19--34.","DOI":"10.1145\/3183713.3196926"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3149193.3149199"},{"key":"e_1_2_1_7_1","unstructured":"Jianhong Tu Ju Fan Nan Tang Peng Wang Chengliang Chai Guoliang Li Ruixue Fan and Xiaoyong Du. 2022. Domain Adaptation for Deep Entity Resolution. In SIGMOD. 443--457.  Jianhong Tu Ju Fan Nan Tang Peng Wang Chengliang Chai Guoliang Li Ruixue Fan and Xiaoyong Du. 2022. Domain Adaptation for Deep Entity Resolution. In SIGMOD. 443--457."},{"key":"e_1_2_1_8_1","volume-title":"arXiv preprint arXiv:1301.3342","author":"Der Maaten Laurens Van","year":"2013","unstructured":"Laurens Van Der Maaten . 2013. Barnes-hut-sne. arXiv preprint arXiv:1301.3342 ( 2013 ). Laurens Van Der Maaten. 2013. Barnes-hut-sne. arXiv preprint arXiv:1301.3342 (2013)."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476332"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3275536.3275541"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:33:18Z","timestamp":1672227198000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554870"}},"subtitle":["hands-off entity resolution with domain adaptation"],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":10,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554870"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554870","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}