Modeling a Born-Digital Factoid Prosopography using the TEI and Linked Data
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Although the TEI has traditionally been used for encoding text, its combination of structured and semi-structured data has made it a compelling choice for born-digital, linked-data resources as well. Our intent here is to demonstrate the advantages it offers for digital prosopographies along with a model that can be used for them. Syriac Persons, Events, and Relations (SPEAR) is a born digital prosopography project in the field of Syriac studies. Where traditional prosopographies focused on prose descriptions of individual persons of significance, SPEAR follows recent developments in research methodologies that instead produce prosopographical factoids. Factoids are structured data about persons drawn from the analysis of historical texts. Most factoid prosopographies use relational databases to model data. Instead, SPEAR uses a customized TEI schema to model factoids that can be queried and visualized in an XML database as well as serialized in HTML for human viewers and in RDF for data sharing. The TEI’s provisions for structured and semi-structured data make it ideal for encoding data from heterogeneous historical source material. Moreover, its linking capabilities connect SPEAR data to related data sets. By modeling prosopographical factoids, and not the source texts themselves, SPEAR offers an example of how a born-digital, data-oriented approach to using the TEI can circumvent some of the challenges posed by the tree structure of XML. It also disrupts traditional understandings of data and stand-off markup through combining Linked Open Data approaches with the use of the TEI.
DescriptionThis journal article is a pre-print. It has been accepted for publication in the Journal of the Text Encoding Initiative pending the approval of revisions requested by reviewers. This PDF was generated from the TEI version of the article submitted to the journal. It was produced in the oXygen XML editor using a transformation scenario provided by the jTEI. Its expected publication date is not currently known.
Schwartz, Daniel L.; Gibson, Nathan P.; Torabi, Katayoun (2021). Modeling a Born-Digital Factoid Prosopography using the TEI and Linked Data. Available electronically from
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