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Intuitive semantic graph tool for enhanced archive exploration

Conference Poster
Publication Date:
2023
abstract:
The paper introduces a new method for visualizing and navigating information in a cultural heritage archive in a simple and intuitive way. The proposed approach employs pre-trained language models to cluster data and create semantic graphs. The creation of multi-layer maps enables deep exploration of archives with large datasets, while the ability to handle multilingual datasets makes it suitable for archives with documents in various languages. These features combine to provide a user-friendly tool that can be adapted to different contexts and provides an overview of archive contents, to allow even non expert users to successfully query the archive.
Iris type:
04.03 Poster in Atti di convegno
Keywords:
Bert; pre-trained language models; transformers; clustering; data visualization; archives; non-expert users
List of contributors:
Gagliardi, Isabella; Artese, MARIA TERESA
Authors of the University:
ARTESE MARIA TERESA
GAGLIARDI ISABELLA
Handle:
https://iris.cnr.it/handle/20.500.14243/461939
Book title:
HT'23: Proceedings of the 34th ACM Conference on Hypertext and Social Media
  • Overview

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URL

https://dl.acm.org/doi/10.1145/3603163.3609069
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