用国会图书馆数据验证AI生成的主题词，提升编目准确率
Better Recommendations: Validating AI-generated Subject Terms Through LOC Linked Data Service
- AI生成主题词后，通过国会图书馆链接数据服务人工核验
- 混合模式使元数据质量更高，解决传统编目效率低问题
- 适合图书馆员和数字资源管理从业者参考
本文探讨将AI生成的主题词融入图书馆编目流程，并通过国会图书馆链接数据服务进行验证。针对《国会图书馆主题词表》体系下传统编目存在的效率低下与积压问题，研究发现生成式AI虽能加速编目，但其分配的主题词准确性不足。为此提出结合AI技术与人工验证的混合方法，利用LOC Linked Data Service实现校验，旨在提升元数据创建的精确性、效率与整体质量，推动图书馆编目实践的智能化升级。
原文摘要 · Abstract (English)
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.
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