Please use this identifier to cite or link to this item: https://repository.ifla.org/handle/123456789/1955
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dc.rights.licenseCC BY 4.0en_US
dc.contributor.authorHahn, Jim-
dc.coverage.spatialLocation::United States of Americaen_US
dc.date.accessioned2022-06-20T19:20:10Z-
dc.date.available2022-06-20-
dc.date.available2022-06-20T19:20:10Z-
dc.date.issued2022-06-20-
dc.identifier.urihttps://2022.ifla.org/-
dc.identifier.urihttps://repository.ifla.org/handle/123456789/1955-
dc.description.abstractAs catalogers begin to integrate linked data descriptions into large-scale discovery graphs through RDF editors, interventions such as semi-automated subject description (http://lcsh.annif.info) are extending and supporting their professional expertise. A large corpus of 9.3 million (9,304,455) title and subject pairs from the IvyPlus Platform for Open Data (POD), along with SVDE bibliographic data, were used for training a semi-automated subject indexing tool for use in BIBFRAME linked data editors. Thereafter, catalogers evaluated the automated subject outputs for inclusion in their descriptions of BIBFRAME resources and the general usefulness of semi-automated subject suggestions. This paper presents the findings of a mixed-methods inquiry to better understand catalogers’ preferences for incorporating machine learning outputs into their work.en_US
dc.language.isoenen_US
dc.publisherInternational Federation of Library Associations and Institutions (IFLA)en_US
dc.relation.ispartofseries87th IFLA World Library and Information Congress (WLIC) / 2022 in Dublin, Ireland;-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectSubject::Linked dataen_US
dc.subjectSubject::Big dataen_US
dc.subjectSubject::Metadataen_US
dc.subjectSubject::Ethicsen_US
dc.titleCataloger acceptance and use of semiautomated subject recommendations for web scale linked data systemsen_US
dc.typeArticlesen_US
dc.typeEvents Materialsen_US
dc.rights.holderJim Hahnen_US
dc.audienceAudience::Big Data Special Interest Groupen_US
ifla.oPubId0en_US
ifla.UnitUnits::Special Interest Group::Big Data Special Interest Groupen_US
Appears in Collections:World Library and Information Congress (WLIC) Materials

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