{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T04:19:58Z","timestamp":1727756398832},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685434","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,25]]},"abstract":"<jats:p>Record linkage across diverse domains is a challenging task with industrial applications ranging from medical records to social media identity linkage. In this study, we present a complex case in the music industry: linking incomplete metadata across domains, from Sound Recordings (SRs) to their corresponding Musical Works (WKs). We present a definition of the problem and highlight its key aspects: comparing record fields beyond conventional string similarity; matching lists of names that only partially align; applying attribute rules, as some attribute values may reflect the quality of information; and applying contextual rules, since the match between an SR and a WK should be evaluated within the context consisting of related WKs. We present a synthetic benchmark that replicates the complexities of the real-world industry problems. While not the focus of the paper, we also report preliminary results of a Transformer-based model that leverages pre-trained embeddings of entity attribute values along with information from the aforementioned key aspects.<\/jats:p>","DOI":"10.3233\/faia240452","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:48:46Z","timestamp":1727689726000},"source":"Crossref","is-referenced-by-count":0,"title":["Cross-Domain Linkage for the Music Industry: An Industrial and Synthetic Benchmark"],"prefix":"10.3233","author":[{"given":"Donghao","family":"Zhang","sequence":"first","affiliation":[{"name":"DTIC, Universitat Pompeu Fabra, 08018, Barcelona, Spain"}]},{"given":"Emilio","family":"Molina","sequence":"additional","affiliation":[{"name":"BMAT Licensing S.L. Barcelona, Spain"}]},{"given":"Aglaia","family":"Galata","sequence":"additional","affiliation":[{"name":"BMAT Licensing S.L. Barcelona, Spain"}]},{"given":"Gon\u00e7al","family":"Calvo","sequence":"additional","affiliation":[{"name":"BMAT Licensing S.L. Barcelona, Spain"}]},{"given":"Denis","family":"Guilhot","sequence":"additional","affiliation":[{"name":"BMAT Licensing S.L. Barcelona, Spain"}]},{"given":"Mart\u00ed","family":"S\u00e1nchez-Fibla","sequence":"additional","affiliation":[{"name":"DTIC, Universitat Pompeu Fabra, 08018, Barcelona, Spain"},{"name":"Artificial Intelligence Research Institute, IIIA (CSIC), Campus UAB, 08193, Spain"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240452","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:48:47Z","timestamp":1727689727000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240452"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,25]]},"ISBN":["9781643685434"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240452","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,25]]}}}