Archaeological data is inherently characterised by vagueness and uncertainty, we use the terms "fuzziness" and "wobbliness" in order to acknowledge that a clear border, reason or source is often missing. In addition, traditional systems often fail to adequately represent it. This paper, developed within the NFDI4Objects consortium's TRAIL 2.2, presents approaches to model and manage such ambiguous information within numismatics and ceramology. In the field of numismatics, the Antike Fundmünzen Europa (AFE) serves as example, demonstrating the capacity to record and represent varying degrees of fuzziness and wobbliness in human-entered data. The paper briefly looks at different Linked Open Data (LOD) modelling strategies for uncertainty, comparing reification (statements about a statement) with a proposed extra node approach, emphasizing pitfalls related to SPARQL queries and transitive properties. One solution we are proposing in order to make usage having fuzziness and wobbliness captured is the Uncertainty Reasoner, which employs Dempster-Shafer theory to combine human input, domain knowledge, and AI confidence levels. This approach explicitly accounts for ignorance, thereby offering more nuanced interpretations. In the domain of ceramology, the Academic Meta Tool (AMT) employs a semantic modelling approach that utilizes weighted edges to articulate ambiguous relations within ceramic datasets. This methodology has been exemplified through an analysis of Samian Ware. The initiative's objective is to incorporate these disparate modelling strategies into NFDI4Objects overarching semantic framework, encompassing OCMDP and MaCHeCO, with the aim of ensuring interoperability and adherence to the FAIR principles. The work's primary objective is to raise awareness about the management of uncertain and unreliable data. It advocates for a standardized approach and underscores the significance of expert oversight, particularly in the context of incorporating AI-generated likelihoods.
Tölle et al. (Wed,) studied this question.