{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:47:34Z","timestamp":1773190054693,"version":"3.50.1"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2023,5,26]]},"abstract":"<jats:p>We present role matching, a novel, fine-grained integrity constraint on temporal fact data, i.e., (subject, predicate, object, timestamp)-quadruples. A role is a combination of subject and predicate and can be associated with different objects as the real world evolves and the data changes over time. A role matching states that the associated object of two or more roles should always match across time. Once discovered, role matchings can serve as integrity constraints to improve data quality, for instance of structured data in Wikipedia[3]. If violated, role matchings can alert data owners or editors and thus allow them to correct the error. Finding all role matchings is challenging due both to the inherent quadratic complexity of the matching problem and the need to identify true matches based on the possibly short history of the facts observed so far.<\/jats:p>\n          <jats:p>To address the first challenge, we introduce several blocking methods both for clean and dirty input data. For the second challenge, the matching stage, we show how the entity resolution method Ditto[27] can be adapted to achieve satisfactory performance for the role matching task. We evaluate our method on datasets from Wikipedia infoboxes, showing that our blocking approaches can achieve 95% recall, while maintaining a reduction ratio of more than 99.99%, even in the presence of dirty data. In the matching stage, we achieve a macro F1-score of 89% on our datasets, using automatically generated labels.<\/jats:p>","DOI":"10.1145\/3588919","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T17:42:05Z","timestamp":1685468525000},"page":"1-26","source":"Crossref","is-referenced-by-count":5,"title":["Matching Roles from Temporal Data: Why Joe Biden is not only President, but also Commander-in-Chief"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9939-4932","authenticated-orcid":false,"given":"Leon","family":"Bornemann","sequence":"first","affiliation":[{"name":"Hasso Plattner Institute, University of Potsdam, Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9517-7707","authenticated-orcid":false,"given":"Tobias","family":"Bleifu\u00df","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, University of Potsdam, Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4180-1384","authenticated-orcid":false,"given":"Dmitri V.","family":"Kalashnikov","sequence":"additional","affiliation":[{"name":"Unaffiliated, NEED LOCATION, APO AA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4710-8719","authenticated-orcid":false,"given":"Fatemeh","family":"Nargesian","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4483-1389","authenticated-orcid":false,"given":"Felix","family":"Naumann","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, University of Potsdam, Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7609-9217","authenticated-orcid":false,"given":"Divesh","family":"Srivastava","sequence":"additional","affiliation":[{"name":"AT&amp;T Chief Data Office, Bedminster, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"1787. U.S. Constitution article II \u00a7 2 cl. 1."},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/1964750.1964776"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-013-9232-5"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/3282495.3282496"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00115"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2396761.2398582"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588560"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732279.2732284"},{"key":"e_1_2_2_9_1","volume-title":"Data matching","author":"Christen Peter","unstructured":"Peter Christen. 2012. The data matching process. In Data matching. Springer, 23--35."},{"key":"e_1_2_2_10_1","volume-title":"Quality measures in data mining","author":"Christen Peter","unstructured":"Peter Christen and Karl Goiser. 2007. Quality and complexity measures for data linkage and deduplication. In Quality measures in data mining. Springer, 127--151."},{"key":"e_1_2_2_11_1","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 125--141","author":"Christen Victor","year":"2019","unstructured":"Victor Christen, Peter Christen, and Erhard Rahm. 2019. Informativeness-based active learning for entity resolution. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 125--141."},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3418896"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687690"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687691"},{"key":"e_1_2_2_15_1","first-page":"1454","article-title":"Distributed Representations of Tuples for Entity Resolution","volume":"11","author":"Ebraheem Muhammad","year":"2018","unstructured":"Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq R. Joty, Mourad Ouzzani, and Nan Tang. 2018. Distributed Representations of Tuples for Entity Resolution. PVLDB 11, 11 (2018), 1454--1467.","journal-title":"PVLDB"},{"key":"e_1_2_2_16_1","unstructured":"Vasilis Efthymiou George Papadakis Kostas Stefanidis and Vassilis Christophides. 2019. MinoanER: Schema-agnostic non-iterative massively parallel resolution of web entities. (2019) 373--384."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1969.10501049"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718504"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-06981-9_2"},{"key":"e_1_2_2_20_1","volume-title":"Proceedings of the International Conference on Management of Data (SIGMOD). 127--138","author":"Mauricio","unstructured":"Mauricio A. Hern\u00e1ndez and Salvatore J. Stolfo. 1995. The Merge\/Purge Problem for Large Databases. In Proceedings of the International Conference on Management of Data (SIGMOD). 127--138."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57454-7_44"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350276"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.14778\/2994509.2994535"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487592"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402733"},{"key":"e_1_2_2_26_1","first-page":"1","article-title":"A survey on truth discovery","volume":"17","author":"Li Yaliang","year":"2016","unstructured":"Yaliang Li, Jing Gao, Chuishi Meng, Qi Li, Lu Su, Bo Zhao, Wei Fan, and Jiawei Han. 2016. A survey on truth discovery. Knowledge Discovery and Data Mining (SIGKDD) Explorations Newsletter 17, 2 (2016), 1--16.","journal-title":"Knowledge Discovery and Data Mining (SIGKDD) Explorations Newsletter"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.14778\/3421424.3421431"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783277"},{"key":"e_1_2_2_29_1","volume-title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR abs\/1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR abs\/1907.11692 (2019). http:\/\/arxiv.org\/abs\/1907.11692"},{"key":"e_1_2_2_30_1","volume-title":"The Four Generations of Entity Resolution","author":"Papadakis George","unstructured":"George Papadakis, Ekaterini Ioannou, Emanouil Thanos, and Themis Palpanas. 2021. The Four Generations of Entity Resolution. Morgan & Claypool Publishers."},{"key":"e_1_2_2_31_1","volume-title":"Article 31 (March","author":"Papadakis George","year":"2020","unstructured":"George Papadakis, Dimitrios Skoutas, Emmanouil Thanos, and Themis Palpanas. 2020. Blocking and Filtering Techniques for Entity Resolution: A Survey. Comput. Surveys 53, 2, Article 31 (March 2020), 42 pages."},{"key":"e_1_2_2_32_1","volume-title":"International Conference on Computational Linguistics (COLING). 877--885","author":"Pasternack Jeff","year":"2010","unstructured":"Jeff Pasternack and Dan Roth. 2010. Knowing what to believe (when you already know something). In International Conference on Computational Linguistics (COLING). 877--885."},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132949"},{"key":"e_1_2_2_34_1","volume-title":"Proceedings of the Extended Semantic Web Conference (ESWC)","volume":"12123","author":"Tanon Thomas Pellissier","unstructured":"Thomas Pellissier Tanon, Gerhard Weikum, and Fabian M. Suchanek. 2020. YAGO 4: A Reason-able Knowledge Base. In Proceedings of the Extended Semantic Web Conference (ESWC), Vol. 12123. Springer, 583--596."},{"key":"e_1_2_2_35_1","first-page":"1","article-title":"Behavior based record linkage","volume":"3","author":"Yakout Mohamed","year":"2010","unstructured":"Mohamed Yakout, Ahmed K Elmagarmid, Hazem Elmeleegy, Mourad Ouzzani, and Alan Qi. 2010. Behavior based record linkage. PVLDB 3, 1--2 (2010), 439--448.","journal-title":"PVLDB"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.190745"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963439"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2661892"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.14778\/2994509.2994534"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3588919","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3588919","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:37Z","timestamp":1750178857000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3588919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,26]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,5,26]]}},"alternative-id":["10.1145\/3588919"],"URL":"https:\/\/doi.org\/10.1145\/3588919","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,26]]}}}