A Generative AI-driven Metadata Modelling Approach (2501.04008v2)

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 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-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.

• The paper introduces a collaborative human-LLM approach that uses a multi-level representation to resolve metadata complexities.
• It combines Generative AI techniques with expert curation to reduce the workload of metadata librarians while enhancing semantic quality.
• The methodology aligns with FAIR principles, promoting improved metadata interoperability and paving a transformative path for digital libraries.

A Generative AI-driven Metadata Modelling Approach

The paper under consideration, authored by Mayukh Bagchi, offers a robust exploration of the challenges and solutions associated with metadata modelling in academic libraries, particularly in the era of Generative AI. This paper is predicated on the observation that conventional static metadata models fail to accommodate the dynamic and heterogeneous requirements of modern information services. The paper postulates that the rigidity of existing metadata frameworks stems from an oversimplification of metadata as a necessary and uniform tool for diverse information domains and settings.

Bagchi advances a contrary perspective, arguing for a nuanced understanding of metadata as a multi-level construct comprising five distinct but interrelated representation levels: perception, terminology, ontology, taxonomy, and intensionality. This ontology-driven schema is designed to address the representational entanglement inherent in traditional metadata models.

Significantly, the paper introduces an innovative approach leveraging Generative AI and Human-LLM collaboration. This approach seeks to disentangle the complexities that arise at each level of representation. The strategy advocates for a representational bijection at each level to explicitly map elements within the metadata model, ensuring a one-to-one correspondence between entities, concepts, and their linguistic, ontological, and taxonomical representations.

Bagchi's solution operates within a Generative AI-driven framework where metadata librarians employ LLMs, through carefully crafted prompt engineering, to generate and refine metadata models. These models are then meticulously validated, repaired, and enriched by librarians, blending human expertise with AI capabilities. The methodology promises to alleviate the intellectual workload traditionally associated with metadata librarianship, while enhancing the semantic quality and interoperability of metadata models.

The implications of this research are profound, extending beyond academic libraries to impact metadata development methodologies and interoperability across various domains. The proposed AI-based disentanglement approach could also inform the FAIR (Findable, Accessible, Interoperative, and Reusable) principles, suggesting a novel interpretation that leverages layered metadata architectures to ensure adherence to FAIR standards.

Moreover, the paper suggests an alignment with contemporary developments in AI and generative technologies as they become increasingly integral to digital libraries. A thorough understanding of how generative AI can facilitate knowledge discovery processes and metadata interoperability is crucial, given the rapid expansion and complexity of digital information infrastructures.

Looking towards future research and practical applications, the paper emphasizes ongoing developments in Human-LLM collaboration as a critical area of paper. There is potential to establish a comprehensive metadata modelling framework that could be adopted across diverse knowledge domains, thus elevating the role of metadata in fostering cross-disciplinary interoperability. Additionally, a cost-benefit analysis of Human-LLM collaboration might provide insights into optimizing this emergent methodology in library and information sciences.

Overall, Bagchi's paper presents a meticulously argued, theoretically grounded, and practically innovative contribution to the discourse on metadata modelling in the age of advanced AI, offering a transformative perspective on knowledge organization in academic settings.