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Conference Papers Year : 2022

Uncertainty Detection in Historical Databases


Historians analyze information from diverse and heterogeneous sources to verify hypotheses and/or to propose new ones. Central to any historical project is the concept of uncertainty, reflecting a lack of confidence. This may limit the scope of the hypotheses formulated. Uncertainty encompasses a variety of aspects including ambiguity, incompleteness, vagueness, randomness, and inconsistency. These aspects cannot be easily detected automatically in plain-text documents. The objective of this article is to propose a process for detecting uncertainty, combining dictionary-based approaches, and pattern identification. The process is validated through experiments conducted on a real historical data set.
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hal-03843641 , version 1 (08-11-2022)



Wissam Mammar Kouadri, Jacky Akoka, Isabelle Comyn-Wattiau, Cedric du Mouza. Uncertainty Detection in Historical Databases. 27th International Conference on Applications of Natural Language to Information Systems (NLDB), Jun 2022, Valencia, Spain. pp.73-85, ⟨10.1007/978-3-031-08473-7_7⟩. ⟨hal-03843641⟩
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