Knowledge modeling and multilingual information extraction for understanding the Cultural Heritage of Silk

Schleider, Thomas
Thesis

Outstanding Doctoral Thesis Award

Modeling any type of human knowledge is a complex effort and needs to consider all specificities of its domain including niche vocabulary. This thesis focuses on such an endeavour for the knowledge about the European silk object production, which can be considered obscure and therefore endangered. However, the fact that such Cultural Heritage data is heterogenous, spread across many museums worldwide, sparse and multilingual poses particular challenges for which knowledge graphs have become more and more popular in recent years. Our main goal is not only into investigating knowledge representations, but also in which ways such an integration process can be accompanied through enrichments, such as information reconciliation through ontologies and vocabularies, as well as metadata predictions to fill gaps in the data. We will first propose a workflow for the management for the integration of data about silk artifacts and afterwards present different classification approaches, with a special focus on unsupervised and zero-shot methods. Finally, we study ways of making exploration of such metadata and images afterwards as easy as possible.


HAL
Type:
Thèse
Date:
2022-09-30
Department:
Data Science
Eurecom Ref:
7016
Copyright:
© EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Thesis and is available at :
See also:

PERMALINK : https://www.eurecom.fr/publication/7016