In the last decade, the research on ancient civilizations has started to rely more and more on data science to extract knowledge on ancient societies from the written sources delivered from the past. In this paper, we combine two well-established frameworks: Linked Data to obtain a rich data structure, and Network Science to explore different research questions regarding the structure and the evolution of ancient societies. We propose a multi-disciplinary pipeline where, starting from a semantically annotated prosopographic archive, a research question is translated into a query on the archive and the obtained dataset is the input to the network model. We applied this pipeline to different archives, a Hittite and a Kassite collection of cuneiform tablets. Finally, network visualization is presented as a powerful tool to highlight both the data structure and the social network analysis results.

Bridging Representation and Visualization in Prosopographic Research: A Case Study

Stefano De Martino;Erica Scarpa;
2022-01-01

Abstract

In the last decade, the research on ancient civilizations has started to rely more and more on data science to extract knowledge on ancient societies from the written sources delivered from the past. In this paper, we combine two well-established frameworks: Linked Data to obtain a rich data structure, and Network Science to explore different research questions regarding the structure and the evolution of ancient societies. We propose a multi-disciplinary pipeline where, starting from a semantically annotated prosopographic archive, a research question is translated into a query on the archive and the obtained dataset is the input to the network model. We applied this pipeline to different archives, a Hittite and a Kassite collection of cuneiform tablets. Finally, network visualization is presented as a powerful tool to highlight both the data structure and the social network analysis results.
2022
AI4CH 2022 - Artificial Intelligence for Cultural Heritage 2022: Proceedings of the 1st Workshop on Artificial Intelligence for Cultural Heritage co-located with the 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5016421
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