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The algorithm is capable of generating linkage rules which select discriminative properties for comparison, apply chains of data transformations to normalize property values, choose appropriate distance measures and thresholds and combine the results of multiple comparisons using non-linear aggregation functions. Our experiments show that the GenLink algorithm outperforms the state-of-the-art genetic programming approach to learning linkage rules recently presented by Carvalho et. al. and is capable of learning linkage rules which achieve a similar accuracy as human written rules for the same problem.<\/jats:p>","DOI":"10.14778\/2350229.2350276","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"1638-1649","source":"Crossref","is-referenced-by-count":60,"title":["Learning expressive linkage rules using genetic programming"],"prefix":"10.14778","volume":"5","author":[{"given":"Robert","family":"Isele","sequence":"first","affiliation":[{"name":"Web-based Systems Group, Freie Universit\u00e4t Berlin, Garystr, Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Bizer","sequence":"additional","affiliation":[{"name":"Web-based Systems Group, Freie Universit\u00e4t Berlin, Garystr., Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807252"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956759"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.4018\/jswis.2009040101"},{"key":"e_1_2_1_5_1","unstructured":"D. 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