Unlocking the Archives. A pipeline for scanning, transcribing and modelling entities of archival documents into Linked Open data.(short paper)

Leon van Wissen, Chiara Latronico, Veruska Zamborlini, Jirsi Reinders, C.M.J.M. van den Heuvel

Research output: Contribution to conferencePaperScientificpeer-review

Abstract

In the project Golden Agents: Creative Industries and the Making of the Dutch Golden Age, heterogeneous resources on the production of the creative industries in the Dutch Golden Age from heritage institutions (e.g. Rijksmuseum, KB, RKD) are brought together as linked data. In this project, the collection of the notarial deeds in the Amsterdam City Archives will provide data on the consumption of cultural goods by the inhabitants of all layers of society in Amsterdam during the Dutch Golden Age. In the project Alle Amsterdams Akten [All Amsterdam Deeds] handwritten notarial deeds are indexed on the level of inventories, documents, person names, and geolocations outside Amsterdam. At the same time, the full text of these documents is being made searchable by using the advanced HTR in the project Crowd Leert Computer Lezen [Crowd Teaches the Computer how to Read].

This data is brought together and analyzed for the occurence of goods in probate and estate inventories. Once extracted and identified, almost all types of these goods can be linked to thesauri such as the Getty’s AAT and reconciled with textual/linguistic references to an item in an external (authored) dataset, such as the STCN, ICONCLASS, and those of the RKD. The model that is used to express this is under development and will be compliant with major and widely used data models in the Galleries, Libraries, Archives, and Museums (GLAM) world, such as the CIDOC-CRM.

In this paper, the full pipeline from archives to annotations is represented that comprehends the successive stages of scanning, indexing, transcribing, correcting, aggregating, and modelling the entities of archival documents into RDF as Linked Open Data. It provides the creation of transparent datasets that can be replicated, evaluated and used for quantitative analyses in digital humanities research.
Original languageEnglish
Publication statusPublished - 03 Jul 2020

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