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http://dx.doi.org/10.25673/119187
Titel: | Linked open data for languages written in cuneiform script |
Autor(en): | Homburg, Timo![]() |
Gutachter: | Mara, Hubert![]() Bruhn, Kai-Christian ![]() Lincke, Eliese-Sophia ![]() |
Körperschaft: | Martin-Luther-Universität Halle-Wittenberg |
Erscheinungsdatum: | 2024 |
Umfang: | 1 Online-Ressource (Seite A-H, xii, 150 Seiten) |
Typ: | Hochschulschrift![]() |
Art: | Dissertation |
Datum der Verteidigung: | 2024-10-24 |
Sprache: | Englisch |
URN: | urn:nbn:de:gbv:3:4-1981185920-1211436 |
Zusammenfassung: | Cuneiform artifacts have a 3000-year-old history and are testimonies of life in some of the oldest civilizations on Earth. The decipherment and translation of cuneiform texts may be broken down into various computer science tasks from OCR to machine translation, all of which depend on and benefit from a well-defined and rich availability of data for the algorithms to work with. This thesis deals with the question of how a data model that describes cuneiform artifacts in different media (e.g., photos, 3D models, transliterations) could be created based on linked open data technologies and which new insights can be gained by exploiting such a data model. Particular emphasis is given to integrating 3D models of cuneiform artifacts and integrating language-independent paleographic descriptions of cuneiform signs. Furthermore, the model should include every aspect of a cuneiform artifact, from the physical artifact down to a single cuneiform wedge, and allow for the exploitation of data useful to many research communities. It therefore presents the building blocks of a linked-data-based digital scholarly edition which will be exemplified with cuneiform research software tools as a practical use case. |
URI: | https://opendata.uni-halle.de//handle/1981185920/121143 http://dx.doi.org/10.25673/119187 |
Open-Access: | ![]() |
Nutzungslizenz: | ![]() |
Enthalten in den Sammlungen: | Interne-Einreichungen |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Dissertation_MLU_2024_HomburgTimo.pdf | 15.34 MB | Adobe PDF | ![]() Öffnen/Anzeigen |