Linked Data Workflows for Community Collections: Experiments with Open Access <scp>AI</scp>

ABSTRACT This paper describes an exploratory study applying an AI chatbot to the transformation of archival collections into linked data representations in a community archive setting. Through qualitative analysis of research diaries, we analyze the experiences of novice data curators using ChatGPT to develop item‐level metadata records conformant to linked data standards based on a collection of informally digitized items. Findings indicate that users find ChatGPT useful for a few, discrete archival processing tasks with limited types of items, but ChatGPT's significant inconsistencies impede systematized workflows. Our findings shed light on anticipated benefits and challenges of using generative AI to create linked data from digital collections in resource‐limited settings, relevant to archival institutions and community organizations seeking low‐barrier opportunities to make their collections more accessible and interconnected online.