Toward Generative AI–Driven Metadata Modeling: A Human–Large Language Model Collaborative Approach

Abstract

For decades, the modeling of metadata has been core to the functioning of any academic library. Metadata’s importance has only increased with the pervasiveness of generative artificial intelligence–driven information activities and services. However, several challenges impact a library metadata model’s reusability, crosswalk, and interoperability with other metadata models. This paper posits that these problems stem from an underlying assumption that there should be only a few core metadata models that would be sufficient for any information service using them, irrespective of the heterogeneity of intradomain or interdomain settings. To that end, this paper advances a contrary view and substantiates its argument in three key steps. First, the paper introduces a novel way of thinking about a library metadata model as an ontology-driven composition of five functionally interlinked representation levels from perception to definition via properties. Second, the paper introduces the representational manifoldness implicit in each of the five levels, which cumulatively contributes to a conceptually entangled library metadata model. Finally, and most importantly, the paper proposes a generative AI–driven, human–large language model collaboration-based metadata modeling approach to disentangle the entanglement inherent in each representation level, which would lead to a conceptually disentangled metadata model. Throughout the paper, the author provides motivating scenarios and examples from libraries handling cancer information.