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This comes at the cost of many irrelevant candidate pairs (i.e., comparisons), which can be significantly reduced by Meta-blocking techniques that leverage the entity co-occurrence patterns inside blocks: first, pairs of candidate entities are weighted in proportion to their matching likelihood, and then, pruning discards the pairs with the lowest scores. Supervised Meta-blocking goes beyond this approach by combining multiple scores per comparison into a feature vector that is fed to a binary classifier. By using probabilistic classifiers, Generalized Supervised Meta-blocking associates every pair of candidates with a score that can be used by any pruning algorithm. For higher effectiveness, new weighting schemes are examined as features. Through extensive experiments, we identify the best pruning algorithms, their optimal sets of features, as well as the minimum possible size of the training set.<\/jats:p>","DOI":"10.14778\/3538598.3538611","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T17:12:31Z","timestamp":1658941951000},"page":"1902-1910","source":"Crossref","is-referenced-by-count":11,"title":["Generalized supervised meta-blocking"],"prefix":"10.14778","volume":"15","author":[{"given":"Luca","family":"Gagliardelli","sequence":"first","affiliation":[{"name":"U. of Modena and Reggio Emilia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Papadakis","sequence":"additional","affiliation":[{"name":"U. of Athens &amp; PPC, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanni","family":"Simonini","sequence":"additional","affiliation":[{"name":"U. of Modena and Reggio Emilia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonia","family":"Bergamaschi","sequence":"additional","affiliation":[{"name":"U. of Modena and Reggio Emilia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Cit\u00e9 &amp; IUF, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"N. 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BLAST2: An Efficient Technique for Loose Schema Information Extraction from Heterogeneous Big Data Sources . ACM J. Data Inf. Qual. 12 , 4 (2020), 18:1--18:22. Domenico Beneventano, Sonia Bergamaschi, Luca Gagliardelli, and Giovanni Simonini. 2020. BLAST2: An Efficient Technique for Loose Schema Information Extraction from Heterogeneous Big Data Sources. ACM J. Data Inf. Qual. 12, 4 (2020), 18:1--18:22.","journal-title":"ACM J. Data Inf. Qual."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2018.02.005"},{"key":"e_1_2_1_4_1","volume-title":"Entity Resolution, and Duplicate Detection","author":"Christen Peter","unstructured":"Peter Christen . 2012. Data Matching - Concepts and Techniques for Record Linkage , Entity Resolution, and Duplicate Detection . Springer . Peter Christen. 2012. Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. 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