Better Recommendations: Validating AI-generated Subject Terms Through LOC Linked Data Service

TL;DR

This paper proposes a hybrid approach combining AI-generated subject terms with human validation via the Library of Congress Linked Data Service to improve accuracy and efficiency in library cataloging.

Contribution

It introduces a validation framework that integrates AI with LOC Linked Data for more reliable subject term assignment in library metadata.

Findings

AI shows promise in speeding up cataloging workflows

Significant limitations exist in AI accuracy for subject headings

Hybrid validation improves metadata quality

Abstract

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.