Better Recommendations: Validating AI-generated Subject Terms Through LOC Linked Data Service
This addresses cataloging backlogs and quality issues for libraries, but it is incremental as it builds on existing AI and validation methods.
The paper tackles the problem of inefficiencies and inaccuracies in AI-generated subject terms for library cataloging by proposing a hybrid approach that combines AI with human validation using the Library of Congress Linked Data Service, aiming to enhance precision and efficiency in metadata creation.
This article explores the integration of AI-generated subject terms into library cataloging, focusing on validation through the Library of Congress Linked Data Service. It examines the challenges of traditional subject cataloging under the Library of Congress Subject Headings system, including inefficiencies and cataloging backlogs. While generative AI shows promise in expediting cataloging workflows, studies reveal significant limitations in the accuracy of AI-assigned subject headings. The article proposes a hybrid approach combining AI technology with human validation through LOC Linked Data Service, aiming to enhance the precision, efficiency, and overall quality of metadata creation in library cataloging practices.