A Generative AI-driven Metadata Modelling Approach

TL;DR

This paper proposes a novel, AI-driven approach to library metadata modeling that uses a layered, ontology-based structure and generative AI collaboration to improve reusability and interoperability of metadata models.

Contribution

It introduces a new ontology-driven, multi-level framework for metadata modeling and leverages generative AI to disentangle complex representations, enhancing metadata model flexibility.

Findings

A five-level ontology-based metadata model is proposed.

Generative AI effectively disentangles complex metadata representations.

The approach improves interoperability and reusability of library metadata models.

Abstract

Since decades, the modelling of metadata has been core to the functioning of any academic library. Its importance has only enhanced with the increasing pervasiveness of Generative Artificial Intelligence (AI)-driven information activities and services which constitute a library's outreach. However, with the rising importance of metadata, there arose several outstanding problems with the process of designing a library metadata model impacting its reusability, crosswalk and interoperability with other metadata models. This paper posits that the above problems stem from an underlying thesis that there should only be a few core metadata models which would be necessary and sufficient for any information service using them, irrespective of the heterogeneity of intra-domain or inter-domain settings. To that end, this paper advances a contrary view of the above thesis and substantiates its argument in three key steps. First, it 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 its intensional definition via properties. Second, it 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, it proposes a Generative AI-driven Human-Large Language Model (LLM) collaboration based metadata modelling approach to disentangle the entanglement inherent in each representation level leading to the generation of a conceptually disentangled metadata model. Throughout the paper, the arguments are exemplified by motivating scenarios and examples from representative libraries handling cancer information.