An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning

El-Hajj, Hassan; Zamani, Mehdi; Büttner, Jochen; Martinetz, Julius; Eberle, Oliver; Shlomi, Noga; Siebold, Anna; Montavon, Grégoire; Müller, Klaus‐Robert; Kantz, Holger; Valleriani, Matteo

doi:10.1007/s13222-022-00414-1

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…These visual elements were collected both manually and with the help of neural networks (Büttner et al, 2022). The Sphaera dataset is stored in a large knowledge graph modeled according to the CIDOC-CRM standards (Bekiari et al, 2021), where information about the editions, as well as fine-grained information about their content is stored (Kräutli & Valleriani, 2018;El-Hajj et al, 2022).…”

Section: Datasetmentioning

confidence: 99%

Explainability and transparency in the realm of digital humanities: toward a historian XAI

El-Hajj,

Eberle,

Merklein

et al. 2023

Int J Digit Humanities

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The recent advancements in the field of Artificial Intelligence (AI) translated to an increased adoption of AI technology in the humanities, which is often challenged by the limited amount of annotated data, as well as its heterogeneity. Despite the scarcity of data it has become common practice to design increasingly complex AI models, usually at the expense of human readability, explainability, and trust. This in turn has led to an increased need for tools to help humanities scholars better explain and validate their models as well as their hypotheses. In this paper, we discuss the importance of employing Explainable AI (XAI) methods within the humanities to gain insights into historical processes as well as ensure model reproducibility and a trustworthy scientific result. To drive our point, we present several representative case studies from the Sphaera project where we analyze a large, well-curated corpus of early modern textbooks using an AI model, and rely on the XAI explanatory outputs to generate historical insights concerning their visual content. More specifically, we show that XAI can be used as a partner when investigating debated subjects in the history of science, such as what strategies were used in the early modern period to showcase mathematical instruments and machines.

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Section: Datasetmentioning

confidence: 99%

Explainability and transparency in the realm of digital humanities: toward a historian XAI

El-Hajj,

Eberle,

Merklein

et al. 2023

Int J Digit Humanities

Self Cite

View full text Add to dashboard Cite

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“…The importance of visual elements in the Sphaera Corpus stems from the nature of the corpus’s editions, which have already been analyzed from very different perspectives [ 21 , 22 , 23 , 24 , 25 , 26 ]. The 359 Sphaera editions, centered on the Tractatus de sphaera by Johannes de Sacrobosco (–1256)and printed between 1472 and 1650, were primarily used to teach geocentric astronomy to university students across Europe, generally between fourteen and eighteen years old [ 27 ].…”

Section: The Sacrobosco Visual Elements Dataset (S...mentioning

confidence: 99%

CorDeep and the Sacrobosco Dataset: Detection of Visual Elements in Historical Documents

et al. 2022

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Recent advances in object detection facilitated by deep learning have led to numerous solutions in a myriad of fields ranging from medical diagnosis to autonomous driving. However, historical research is yet to reap the benefits of such advances. This is generally due to the low number of large, coherent, and annotated datasets of historical documents, as well as the overwhelming focus on Optical Character Recognition to support the analysis of historical documents. In this paper, we highlight the importance of visual elements, in particular illustrations in historical documents, and offer a public multi-class historical visual element dataset based on the Sphaera corpus. Additionally, we train an image extraction model based on YOLO architecture and publish it through a publicly available web-service to detect and extract multi-class images from historical documents in an effort to bridge the gap between traditional and computational approaches in historical studies.

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Synergize Multidisciplinary Research via Research Data Management

Sharma,

Suryani,

Strohm

et al. 2023

2023 IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)

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An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning

Cited by 4 publications

References 25 publications

Explainability and transparency in the realm of digital humanities: toward a historian XAI

Explainability and transparency in the realm of digital humanities: toward a historian XAI

CorDeep and the Sacrobosco Dataset: Detection of Visual Elements in Historical Documents

Synergize Multidisciplinary Research via Research Data Management

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