Dutch Ships and Sailors Linked Data

Boer, Victor de; Rossum, Matthias van; Leinenga, J.; Hoekstra, Rik

doi:10.1007/978-3-319-11964-9_15

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…The aim is often to harmonize heterogeneous datasets to create a single integrated, consistent and unambiguous dataset (Ashkpour, Meroño-Peñuela and Mandemakers 2015). As historical materials are rarely complete, consistent and unambiguous, harmonization in the context of historical research is only partly attainable and each process of harmonization should be tailored to a specific (set of) research question(s) ( de Boer et al 2015).…”

Section: Linkingmentioning

confidence: 99%

Data scopes for digital history research

Hoekstra

Koolen

2018

Historical Methods: A Journal of Quantitative and Interdiscipli

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Section: Linkingmentioning

confidence: 99%

Data scopes for digital history research

Hoekstra

Koolen

2018

Historical Methods: A Journal of Quantitative and Interdiscipli

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“…In order to to make the cedar rdf Database a 5-star Linked Dataset, we issue links as depicted by Figure 3. Concretely, we use expert knowledge to link the census data to: (1) occupations in the the Historical International Standard Classification of Occupations (hisco) ( van Leeuwen, Maas & Miles, 2002); (2) occupations in the iconclass19 system, with alternative occupation descriptions; (3) to historical Dutch municipalities in gemeentegeschiedenis.nl (Zandhuis, den Engelse & Mac Gillavry, 2015), which link to DBpedia, GeoNames, codes of the cbs (Centraal Bureau voor de Statistiek), and codes of the Amsterdamse Code (van der Meer & Boonstra, 2006); and to maritime trade registers of the Dutch Ships and Sailors dataset (de Boer, Leinenga, van Rossum & Hoekstra, 2014).…”

Section: Datamentioning

confidence: 99%

Improving Access to the Dutch Historical Censuses with Linked Open Data

Meroño‐Peñuela

Ashkpour

Gilissen³

et al. 2018

Res. Data J Humanit. Soc. Sci.

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The Dutch Historical Censuses (1795–1971) contain statistics that describe almost two centuries of History in the Netherlands. These censuses were conducted once every 10 years (with some exceptions) from 1795 to 1971. Researchers have used its wealth of demographic, occupational, and housing information to answer fundamental questions in social economic history. However, accessing these data has traditionally been a time consuming and knowledge intensive task. In this paper, we describe the outcomes of the cedar project, which make access to the digitized assets of the Dutch Historical Censuses easier, faster, and more reliable. This is achieved by using the data publishing paradigm of Linked Data from the Semantic Web. We use a digitized sample of 2,288 census tables to produce a linked dataset of more than 6.8 million statistical observations. The dataset is modeled using the rdf Data Cube, Open Annotation, and prov vocabularies. The contributions of representing this dataset as Linked Data are: (1) a uniform database interface for efficient querying of census data; (2) a standardized and reproducible data harmonization workflow; and (3) an augmentation of the dataset through richer connections to related resources on the Web.

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“…Coupling them with background knowledge, and linking them through formal descriptions, provides connectivity throughout the documents [21]. Work has already been done linking collections and items using the principles of linked data, not only regarding biodiversity [15,28], but cultural heritage collections in general [9,7,8,6,10,11]. Fewer examples exist where the content of items in such collections are semantically linked [7].…”

Section: Introductionmentioning

confidence: 99%

Semantic annotation of natural history collections

Stork¹,

Weber

Miracle

et al. 2019

Journal of Web Semantics

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Large collections of historical biodiversity expeditions are housed in natural history museums throughout the world. Potentially they can serve as rich sources of data for cultural historical and biodiversity research. However, they exist as only partially catalogued specimen repositories and images of unstructured, non-standardised, handwritten text and drawings. Although many archival collections have been digitised, disclosing their content is challenging. They refer to historical place names and outdated taxonomic classifications and are written in multiple languages. Efforts to transcribe the handwritten text can make the content accessible, but semantically describing and interlinking the content would further facilitate research. We propose a semantic model that serves to structure the named entities in natural history archival collections. In addition, we present an approach for the semantic annotation of these collections whilst documenting their provenance. This approach serves as an initial step for an adaptive learning approach for semi-automated extraction of named entities from natural history archival collections. The applicability of the semantic model and the annotation approach is demonstrated using image scans from a collection of 8,000 field book pages gathered by the Committee for Natural History of the Netherlands Indies between 1820 and 1850, and evaluated together with domain experts from the field of natural and cultural history.

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Dutch Ships and Sailors Linked Data

Cited by 13 publications

References 8 publications

Data scopes for digital history research

Data scopes for digital history research

Improving Access to the Dutch Historical Censuses with Linked Open Data

Semantic annotation of natural history collections

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