Analysis of the Usability of Automatically Enriched Cultural Heritage Data
Julien Antoine Raemy, Robert Sanderson
备注：
This is the preprint version of a chapter submitted to be included in the book "Decoding Cultural Heritage: a critical dissection and taxonomy of human creativity through digital tools", to be published by Springer Nature. The chapter is currently undergoing peer review for potential inclusion in the book
本章以 LOUD 原则为基础，结合 IIIF、Linked Art 及耶鲁 LUX 平台案例，分析自动丰富的文化遗产关联数据在语义互操作中的可用性、收益与挑战。
摘要：
本章阐述了文化遗产资源在互操作性与标准化数据发布方面的潜力，重点关注由社区驱动的方法以及面向可用性的网络标准。以 JSON-LD 作为通用语言的 Linked Open Usable Data（LOUD，关联开放可用数据）设计原则构成了研究基础。作者首先探讨了 International Image Interoperability Framework（IIIF，国际图像互操作性框架）在推动图像类资源互操作性方面取得的重大进展。IIIF 的原则与实践为 Linked Art 铺平了道路；Linked Art 通过展示关联数据如何轻松促进跨门户、跨机构的语义化文化遗产数据整合与共享，扩展了关联数据的应用。为对所讨论的概念进行实践演示，本章重点介绍了耶鲁大学馆藏发现平台 LUX 的实施情况。LUX 作为大规模使用关联数据的极具说服力的案例研究，展示了自动丰富在文化遗产领域的真实应用。本章提出的分析植根于实证研究，深入考察了社区实践与语义互操作的更广泛背景。通过审视协作努力以及各类文化遗产资源的整合，该研究揭示了与 LOUD 相关的潜在收益与挑战。
英文摘要：
This chapter presents the potential of interoperability and standardised data publication for cultural heritage resources, with a focus on community-driven approaches and web standards for usability. The Linked Open Usable Data (LOUD) design principles, which rely on JSON-LD as lingua franca, serve as the foundation.
We begin by exploring the significant advances made by the International Image Interoperability Framework (IIIF) in promoting interoperability for image-based resources. The principles and practices of IIIF have paved the way for Linked Art, which expands the use of linked data by demonstrating how it can easily facilitate the integration and sharing of semantic cultural heritage data across portals and institutions.
To provide a practical demonstration of the concepts discussed, the chapter highlights the implementation of LUX, the Yale Collections Discovery platform. LUX serves as a compelling case study for the use of linked data at scale, demonstrating the real-world application of automated enrichment in the cultural heritage domain.
Rooted in empirical study, the analysis presented in this chapter delves into the broader context of community practices and semantic interoperability. By examining the collaborative efforts and integration of diverse cultural heritage resources, the research sheds light on the potential benefits and challenges associated with LOUD.
The "Collections as ML Data" Checklist for Machine Learning & Cultural Heritage
University of Washington（华盛顿大学）
在文化遗产部门，在将机器学习技术应用于数字收藏时，越来越多地协同努力考虑关键的社会技术视角。虽然文化遗产界已经集体开发了一套新的工作，详细说明了图书馆和其他文化遗产机构在组织层面上负责机器学习的操作，但仍然缺乏专门为从事机器学习项目的从业人员制定的指导方针。将机器学习应用于文化遗产所涉及的多方面利害关系和敏感性强调了制定此类指南的重要性。本文通过制定一个详细的清单，指导问题和做法，可以采用，而开发一个机器学习项目，利用文化遗产数据，有助于这一需要。我将生成的检查表称为“Collections as ML Data”检查表，完成后，可以与项目的可交付成果一起发布。通过调查现有的项目，包括我自己的项目Newspaper Navigator，我证明了“收集ML数据”清单的合理性，并展示了如何使用和操作制定的指导问题。
Rich Interoperable Metadata for Cultural Heritage Projects at Jagiellonian University
Department of Human-Centered Artificial Intelligence, Institute of Applied Computer Science, Faculty of Physics, Astronomy and Applied Computer Science（人类中心人工智能系、应用计算机科学研究所、物理、天文学与应用计算机科学学院）；Lukasiewicz Research Network -- Institute of Innovative Technologies EMAG（卢卡西维茨研究网络——创新技术EMAG研究所）
Augmenting Italian Cultural Heritage with Virtual and Digital Technologies: the final outcomes of Project CHANGES' Spoke 4
University of Bologna（博洛尼亚大学）；University of Suor Orsola Benincasa（苏奥尔索拉贝宁卡萨大学）；University of Turin（都灵大学）；National Research Council（国家研究委员会）；Robin Studio（罗宾工作室）
Saving temporary exhibitions in virtual environments: the Digital Renaissance of Ulisse Aldrovandi -- acquisition and digitisation of cultural heritage objects
Cloud-based digitization workflow with rich metadata acquisition for cultural heritage objects
Department of Human-Centered Artificial Intelligence, Institute of Applied Computer Science, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University（以人为本的人工智能系、应用计算机科学研究所、物理、天文学和应用计算机科学学院，雅盖隆大学）；Faculty of Humanities, AGH University of Krakow（人文学院，克拉科夫AGH大学）
From Queries to Narratives: Cultural Heritage Data Stories for Knowledge Graph Exploration and Quality Assessment
FIZ Karlsruhe – Leibniz Institute for Information Infrastructure（FIZ卡尔斯鲁厄–莱布尼茨信息基础设施研究所）；Institute of Applied Informatics and Formal Description Methods (AIFB) of KIT（卡尔斯鲁厄理工学院应用信息学与形式描述方法研究所）；Academy of Sciences and Literature Mainz（美因茨科学与文学院）
Digital hybridity and relics in cultural heritage: using corpus linguistics to inform design in emerging technologies from AI to VR
School of English, University of Nottingham（诺丁汉大学英语学院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；Department of Modern Languages, Aberystwyth University（阿伯里斯特威斯大学现代语言系）
