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.

MARC and LCSH Systems Library metadata practitioners usually produce bibliographic records for library resources in the form of MARC (Machine-Readable Cataloging) records. MARC is a data format that organizes information about books and other materials in a library collection. One important part of the data is the MARC 6XX Subject Access Fields, which are used to input subject access entries and terms (e.g., topical terms, personal names, places, and time periods) covered by each book or resource. Most of these fields contain subject terms based on controlled vocabularies, one of which is the Library of Congress Subject Headings (LCSH). A key feature of the LCSH system is the application of subdivisions, allowing catalogers and indexers to construct very precise and multi-faceted subject strings by combining main headings with relevant topical, form, chronological and geographic subdivisions in a systematic way.