IT- and machine learning-based methods of classification: The cooperative project ClaReNet – Classification and Representation for Networks

The classification of archaeological finds and their representation are shaped by various object epistemological approaches and changes of medium. With ever increasing digitisation, there are now new possibilities of classification, for example using methods of automatic image recognition, as well as the representation of finds on the web with linked open data. ClaReNet, a cooperative project of the Römisch-Germanische Kommission (German Archaeological Institute) and the Big Data Lab (Goethe University Frankfurt), funded by the Bundesministerium für Bildung und Forschung (BMBF; Federal Ministry of Education and Research), tests the possibilities and limits of new digital methods of classification and representation. To this end, traditional approaches of typification and the recording of attributes in numismatics and archaeology are compared with IT-based methods of classification, including deep learning, using the examples of three Celtic coin series that were each chosen to address particular research questions and problems. This work is accompanied by a science and technology study (STS) PANDA, which focuses on Path dependencies, Actor Networks and Digital Agency. This paper briefly introduces the approach of object epistemologies before considering Celtic coins as scientific objects and the history of research on them with regard to classifications and typologies. Using the example of a series of coins from Armorica (Brittany), we will present how deep learning classifies the coinage, how this may differ from classifications by numismatists, and the lessons that are to be learned from this exercise. From an STS perspective, we analyse the actor network that emerges during image data processing. The paper concludes with a reflection on the transformation of numismatic practices resulting from the IT-based methods used in the project, as well as an outlook on further possibilities for research into the classification and representation of Celtic coins.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-18
DOI
https://doi.org/10.5281/zenodo.7341342
Citations
1
Primary Topic
Currency Recognition and Detection
Type
article
Field-Weighted Citation Impact
0.25
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article

IT- and machine learning-based methods of classification: The cooperative project ClaReNet – Classification and Representation for Networks

1 citations
Zenodo (CERN European Organization for Nuclear Research)
Currency Recognition and Detection
0.25
article

IT- and machine learning-based methods of classification: The cooperative project ClaReNet – Classification and Representation for Networks

article en
1 citations

Abstract

The classification of archaeological finds and their representation are shaped by various object epistemological approaches and changes of medium. With ever increasing digitisation, there are now new possibilities of classification, for example using methods of automatic image recognition, as well as the representation of finds on the web with linked open data. ClaReNet, a cooperative project of the Römisch-Germanische Kommission (German Archaeological Institute) and the Big Data Lab (Goethe University Frankfurt), funded by the Bundesministerium für Bildung und Forschung (BMBF; Federal Ministry of Education and Research), tests the possibilities and limits of new digital methods of classification and representation. To this end, traditional approaches of typification and the recording of attributes in numismatics and archaeology are compared with IT-based methods of classification, including deep learning, using the examples of three Celtic coin series that were each chosen to address particular research questions and problems. This work is accompanied by a science and technology study (STS) PANDA, which focuses on Path dependencies, Actor Networks and Digital Agency. This paper briefly introduces the approach of object epistemologies before considering Celtic coins as scientific objects and the history of research on them with regard to classifications and typologies. Using the example of a series of coins from Armorica (Brittany), we will present how deep learning classifies the coinage, how this may differ from classifications by numismatists, and the lessons that are to be learned from this exercise. From an STS perspective, we analyse the actor network that emerges during image data processing. The paper concludes with a reflection on the transformation of numismatic practices resulting from the IT-based methods used in the project, as well as an outlook on further possibilities for research into the classification and representation of Celtic coins.

Zenodo (CERN European Organization for Nuclear Research)
Goethe University Frankfurt (DE), Deutsches Archäologisches Institut, Zentrale (DE)
Quality Education
Openalex Percentile: Top 41%
Currency Recognition and Detection
0.25
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