一种生成式AI驱动的元数据建模方法
A Generative AI-driven Metadata Modelling Approach
AI总结：
针对图书馆元数据模型可重用性与互操作性问题，本文提出五层本体组合与表示流形性视角，并采用生成式AI驱动的人-LLM协作方法构建概念解纠缠的元数据模型。
摘要：
数十年来，元数据建模一直是任何学术图书馆运作的核心。随着生成式人工智能（AI）驱动的、构成图书馆外展服务的信息活动和服务日益普及，其重要性只增不减。然而，随着元数据重要性不断上升，图书馆元数据模型的设计过程出现了若干突出问题，影响其可重用性、跨模式映射以及与其他元数据模型的互操作性。本文认为，上述问题源于一个潜在论点：无论领域内或领域间环境的异质性如何，任何使用元数据模型的信息服务都只需要少数几个核心元数据模型，且这些模型既是必要的也是充分的。为此，本文提出与上述论点相反的观点，并通过三个关键步骤论证其立场。首先，本文引入一种思考图书馆元数据模型的新方式，将其视为由本体驱动、由五个功能互连的表示层组成的结构：从感知开始，经由属性，直至其内涵定义。其次，本文介绍隐含在五个层级中的表示流形性，这种性质累积性地促成一个概念上纠缠的图书馆元数据模型。最后，也是最重要的，本文提出一种生成式AI驱动的、基于人类与大型语言模型（LLM）协作的元数据建模方法，用以解开每个表示层中固有的纠缠，从而生成一个概念上解纠缠的元数据模型。在全文中，相关论点均通过处理癌症信息的代表性图书馆所提供的启发性场景和示例加以说明。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
