Published September 7, 2023 | Version v1
Poster Open

Text Encoding without //text. The use of //abstract as means to avoid the one-dimensionality of ego-networks in ›Buber-Correspondences Digital‹ project

  • 1. ROR icon Goethe University Frankfurt
  • 2. ROR icon Academy of Sciences and Literature

Description

‘Buber-Correspondences Digital’ is a project focused on the 41,400 preserved letters, postcards, and telegrams exchanged between Martin Buber and over 7,000 different correspondents. The goal of the project is to thoroughly research these dialogues in epistolary-form, which have received little attention thus far. A tiered editorial process is being applied using the Text Encoding Initiative (TEI). A suitable framework for the elementary metadata, such as sender, recipient, location, and date, is provided by the TEI element , which will be made available for all correspondence units. In addition, 20% of the letters, mainly those with notable figures, are being digitally edited in full text using the TEI. 65% of the correspondences are provided with extensive structured metadata using the TEI element .

Each abstract is divided into three parts: First, it contains a (human-readable) summary of essential contents organized in s. Followed by indices listing the entities (persons, organizations, places, works, events), both those explicitly mentioned and those implicitly relevant. With regard to the planned cultural-historical analysis of Buber’s dialogical relationships and networks of scholars and intellectuals represented by the correspondences, the project takes a third step: the entities are brought into relation with each other, thus breaking up the purely static presentation of a plain register. This dynamization is achieved with the help of the TEI element , which interrelates entities in a triple-like structure: subject-predicate-object. By focusing on the content of letters, the ‘boring’ one-dimensionality that ego-networks tend to have can be overcome.

Initial evaluations of this dynamic structuralization of letter content have shown promising results, using RDF/SPARQL and graph-database Neo4j.

More information: https://gitlab.rlp.net/adwmainz/digicademy/bkd/bkd-presentations/teimec 

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