{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:21:58Z","timestamp":1793132518779,"version":"4.5.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>\n            Despite the efforts in 70+ years in all aspects of entity resolution (ER), there is still a high demand for democratizing ER - by reducing the heavy human involvement in labeling data, performing feature engineering, tuning parameters, and defining blocking functions. With the recent advances in deep learning, in particular distributed representations of words (\n            <jats:italic>a.k.a<\/jats:italic>\n            . word embeddings), we present a novel ER system, called D\n            <jats:sc>eep<\/jats:sc>\n            ER, that achieves good accuracy, high efficiency, as well as ease-of-use (\n            <jats:italic>i.e<\/jats:italic>\n            ., much less human efforts). We use sophisticated composition methods, namely uni- and bi-directional recurrent neural networks (RNNs) with long short term memory (LSTM) hidden units, to convert each tuple to a distributed representation (\n            <jats:italic>i.e<\/jats:italic>\n            ., a vector), which can in turn be used to effectively capture similarities between tuples. We consider both the case where pre-trained word embeddings are available as well the case where they are not; we present ways to learn and tune the distributed representations that are customized for a specific ER task under different scenarios. We propose a locality sensitive hashing (LSH) based blocking approach that takes all attributes of a tuple into consideration and produces much smaller blocks, compared with traditional methods that consider only a few attributes. We evaluate our algorithms on multiple datasets (including benchmarks, biomedical data, as well as multi-lingual data) and the extensive experimental results show that D\n            <jats:sc>eep<\/jats:sc>\n            ER outperforms existing solutions.\n          <\/jats:p>","DOI":"10.14778\/3236187.3236198","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1454-1467","source":"Crossref","is-referenced-by-count":176,"title":["Distributed representations of tuples for entity resolution"],"prefix":"10.14778","volume":"11","author":[{"given":"Muhammad","family":"Ebraheem","sequence":"first","affiliation":[{"name":"Qatar Computing Research Institute, HBKU, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saravanan","family":"Thirumuruganathan","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute, HBKU, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shafiq","family":"Joty","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mourad","family":"Ouzzani","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute, HBKU, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Tang","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute, HBKU, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,10,5]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Benchmark datasets for entity resolution. https:\/\/dbs.uni-leipzig.de\/en\/research\/projects\/object_matching\/fever\/benchmark_datasets_for_entity_resolution.  Benchmark datasets for entity resolution. https:\/\/dbs.uni-leipzig.de\/en\/research\/projects\/object_matching\/fever\/benchmark_datasets_for_entity_resolution."},{"key":"e_1_2_1_2_1","unstructured":"Duplicate detection record linkage and identity uncertainty: Datasets. http:\/\/www.cs.utexas.edu\/users\/ml\/riddle\/data.html.  Duplicate detection record linkage and identity uncertainty: Datasets. http:\/\/www.cs.utexas.edu\/users\/ml\/riddle\/data.html."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0141287"},{"key":"e_1_2_1_4_1","volume-title":"SIGKDD Workshop on Data Cleaning, Record Linkage, and Object Consolidation","author":"Baxter R.","year":"2003"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Y. Bengio A. Courville and P. Vincent. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35(8):1798--1828 2013.  Y. Bengio A. Courville and P. Vincent. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35(8):1798--1828 2013.","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_2_1_6_1","volume-title":"JMLR","author":"Bengio Y.","year":"2003"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956759"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"P. Bojanowski E. Grave A. Joulin and T. Mikolov. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 2016.  P. Bojanowski E. Grave A. Joulin and T. Mikolov. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 2016.","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_2_1_10_1","volume-title":"Database","author":"Chen Q.","year":"2016"},{"key":"e_1_2_1_11_1","volume-title":"Manning Publications","author":"Chollet F.","year":"2018"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.127"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775116"},{"key":"e_1_2_1_14_1","volume-title":"NIPS Workshop","author":"Collobert R.","year":"2011"},{"key":"e_1_2_1_15_1","volume-title":"JMLR","author":"Collobert R.","year":"2011"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2009.4959971"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3035960"},{"key":"e_1_2_1_18_1","unstructured":"S. Das A. Doan P. S. G. C. C. Gokhale and P. Konda. The magellan data repository. https:\/\/sites.google.com\/site\/anhaidgroup\/projects\/data.  S. Das A. Doan P. S. G. C. C. Gokhale and P. Konda. The magellan data repository. https:\/\/sites.google.com\/site\/anhaidgroup\/projects\/data."},{"key":"e_1_2_1_19_1","volume-title":"HILDA@SIGMOD","author":"Doan A.","year":"2017"},{"issue":"12","key":"e_1_2_1_20_1","article-title":"Record linkage","volume":"36","author":"Dunn H. L.","year":"1946","journal-title":"American Journal of Public Health"},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"A. K. Elmagarmid P. G. Ipeirotis and V. S. Verykios. Duplicate record detection: A survey. TKDE 19(1) 2007.  A. K. Elmagarmid P. G. Ipeirotis and V. S. Verykios. Duplicate record detection: A survey. TKDE 19(1) 2007.","DOI":"10.1109\/TKDE.2007.250581"},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","unstructured":"M. Faruqui J. Dodge S. K. Jauhar C. Dyer E. Hovy and N. A. Smith. Retrofitting word vectors to semantic lexicons. arXiv preprint arXiv:1411.4166 2014.  M. Faruqui J. Dodge S. K. Jauhar C. Dyer E. Hovy and N. A. Smith. Retrofitting word vectors to semantic lexicons. arXiv preprint arXiv:1411.4166 2014.","DOI":"10.3115\/v1\/N15-1184"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1969.10501049"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783396"},{"key":"e_1_2_1_25_1","first-page":"518","volume-title":"PVLDB","volume":"99","author":"Gionis A.","year":"1999"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588576"},{"key":"e_1_2_1_27_1","volume-title":"MIT Press","author":"Goodfellow I.","year":"2016"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/276698.276876"},{"key":"e_1_2_1_30_1","volume-title":"Information Systems","author":"Kenig B.","year":"2013"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.14778\/3007263.3007314"},{"key":"e_1_2_1_32_1","volume-title":"QDB\/MUD","author":"K\u00f6pcke H.","year":"2008"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920904"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1027"},{"key":"e_1_2_1_35_1","volume-title":"ICML","author":"Le Q.","year":"2014"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1278"},{"key":"e_1_2_1_37_1","first-page":"950","volume-title":"PVLDB","author":"Lv Q.","year":"2007"},{"key":"e_1_2_1_38_1","unstructured":"Magellan. End-to-end em workflows 2017.  Magellan. End-to-end em workflows 2017."},{"key":"e_1_2_1_39_1","first-page":"440","volume-title":"AAAI","author":"Michelson M.","year":"2006"},{"key":"e_1_2_1_40_1","volume-title":"NIPS","author":"Mikolov T.","year":"2013"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196926"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/1841211"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775087"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.14778\/3149193.3149199"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11257-2_20"},{"key":"e_1_2_1_48_1","unstructured":"S. Thirumuruganathan N. Tang and M. Ouzzani. Data curation with deep learning : Towards self driving data curation. arXiv preprint arXiv:1803.01384 2018.  S. Thirumuruganathan N. Tang and M. Ouzzani. Data curation with deep learning : Towards self driving data curation. arXiv preprint arXiv:1803.01384 2018."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350263"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.14778\/2021017.2021020"},{"key":"e_1_2_1_51_1","unstructured":"J. Wang H. T. Shen J. Song and J. Ji. Hashing for similarity search: A survey. arXiv preprint arXiv:1408.2927 2014.  J. Wang H. T. Shen J. Song and J. Ji. Hashing for similarity search: A survey. arXiv preprint arXiv:1408.2927 2014."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498378"},{"key":"e_1_2_1_53_1","doi-asserted-by":"crossref","first-page":"550","DOI":"10.4018\/978-1-60566-010-3.ch086","volume-title":"Encyclopedia of Data Warehousing and Mining","author":"Winkler W. E.","year":"2009"},{"key":"e_1_2_1_54_1","unstructured":"J. Yosinski J. Clune Y. Bengio and H. Lipson. How transferable are features in deep neural networks? In Advances in neural information processing systems pages 3320--3328 2014.  J. Yosinski J. Clune Y. Bengio and H. Lipson. How transferable are features in deep neural networks? In Advances in neural information processing systems pages 3320--3328 2014."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3236187.3236198","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:44:41Z","timestamp":1672220681000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3236187.3236198"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":54,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2018,7]]}},"alternative-id":["10.14778\/3236187.3236198"],"URL":"https:\/\/doi.org\/10.14778\/3236187.3236198","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,7]]}}}