Harmonizing and publishing heterogeneous premodern manuscript metadata as Linked Open Data. (26th May 2021)

• Record Type: Journal Article
• Authors: Koho, Mikko; Burrows, Toby; Hyvönen, Eero; Ikkala, Esko; Page, Kevin; Ransom, Lynn; Tuominen, Jouni; Emery, Doug; Fraas, Mitch; Heller, Benjamin; Lewis, David; Morrison, Andrew; Porte, Guillaume; Thomson, Emma; Velios, Athanasios; Wijsman, Hanno
• Is Part Of: Journal of the Association for Information Science and Technology. Volume 73:Number 2(2022)
• Page Start: 240
• Page End: 257
• Publication Date: 2021-05-26
• Subjects: Information science -- Periodicals; Information technology -- Periodicals; 020.5
• DOI: 10.1002/asi.24499
• Languages: English
• ISSNs: 2330-1635

Abstract

Abstract: Manuscripts are a crucial form of evidence for research into all aspects of premodern European history and culture, and there are numerous databases devoted to describing them in detail. This descriptive information, however, is typically available only in separate data silos based on incompatible data models and user interfaces. As a result, it has been difficult to study manuscripts comprehensively across these various platforms. To address this challenge, a team of manuscript scholars and computer scientists worked to create "Mapping Manuscript Migrations" (MMM), a semantic portal, and a Linked Open Data service. MMM stands as a successful proof of concept for integrating distinct manuscript datasets into a shared platform for research and discovery with the potential for future expansion. This paper will discuss the major products of the MMM project: a unified data model, a repeatable data transformation pipeline, a Linked Open Data knowledge graph, and a Semantic Web portal. It will also examine the crucial importance of an iterative process of multidisciplinary collaboration embedded throughout the project, enabling humanities researchers to shape the development of a digital platform and tools, while also enabling the same researchers to ask more sophisticated and comprehensive research questions of the aggregated data.