Abstract

Cultural heritage (CH) contents are typically strongly interlinked, but published in heterogeneous, distributed local data silos, making it difficult to utilize the data on a global level. Furthermore, the content is usually available only for humans to read, and not as data for Digital Humanities (DH) analyses and application development. This application report addresses these problems by presenting a collaborative publication model for CH Linked Data and six design principles for creating shared data services and semantic portals for DH research and applications. This Sampo model has evolved gradually in 2002–2021 through lessons learned when developing the Sampo series of linked data services and semantic portals in use, including MuseumFinland (2004), CultureSampo (2009), BookSampo (2011), WarSampo (2015), Norssit Alumni (2017), U.S. Congress Prosopographer (2018), NameSampo (2019), BiographySampo (2019), WarVictimSampo 1914–1922 (2019), MMM (2020), AcademySampo (2021), FindSampo (2021), WarMemoirSampo (2021), and LetterSampo (2022). These Semantic Web applications surveyed in this paper cover a wide range of application domains in CH and have attracted up to millions of users on the Semantic Web, suggesting feasibility of the proposed Sampo model. This work shows a shift of focus in research on CH semantic portals from data aggregation and exploration systems (1. generation systems) to systems supporting DH research (2. generation systems) with data analytic tools, and finally to automatic knowledge discovery and Artificial Intelligence (3. generation systems).

中文翻译：

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语义网上的数字人文：Sampo 模型和门户系列

摘要

文化遗产 (CH) 内容通常具有很强的相互关联性，但发布在异构的分布式本地数据孤岛中，因此难以在全球范围内利用这些数据。此外，内容通常仅供人类阅读，而不是作为数字人文 (DH) 分析和应用程序开发的数据。本应用报告通过展示 CH Linked Data 的协作发布模型和为 DH 研究和应用创建共享数据服务和语义门户的六项设计原则来解决这些问题。这种Sampo 模型在 2002-2021 年通过开发Sampo 系列时的经验逐渐演变使用中的链接数据服务和语义门户，包括 MuseumFinland (2004)、CultureSampo (2009)、BookSampo (2011)、WarSampo (2015)、Norssit Alumni (2017)、美国国会 Prosopographer (2018)、NameSampo (2019)、BiographySampo (2019)、WarVictimSampo 1914–1922 (2019)、MMM (2020)、AcademySampo (2021)、FindSampo (2021)、WarMemoirSampo (2021) 和 LetterSampo (2022)。本文调查的这些语义网应用程序涵盖了 CH 中广泛的应用程序领域，并在语义网上吸引了多达数百万的用户，这表明了所提出的 Sampo 模型的可行性。这项工作显示了 CH 语义门户研究的重点从数据聚合和探索系统（1. 生成系统）转移到支持 DH 研究的系统（2. 生成系统）与数据分析工具，