Cite this as: Katsianis, M., Bruseker, G., Nenova, D., Marlet, O., Hivert, F., Hiebel, G., Ore, C.-E., Derudas, P., Opitz , R.and Uleberg, E. 2023 Semantic Modelling of Archaeological Excavation Data. A review of the current state of the art and a roadmap of activities, Internet Archaeology 64. https://doi.org/10.11141/ia.64.12
Archaeological data repositories usually manage excavation data collections as project-level entities with restricted capacities to facilitate search or aggregation of excavation data at the sub-collection level (trenches, finds, season reports or excavation diaries etc.). More granular access to excavation data collections would enable layered querying across their informational content. In the past decade, several attempts to adapt CIDOC CRM in order to provide more explicit descriptions of the excavation universe have resulted in the use of domain-specific model extensions (e.g. CRMarchaeo, CRMsci, CRMba). Each focuses on corresponding aspects of the excavation research process, while their combined usage has potential to support expressive data mappings at the sub-collection level. As part of the ARIADNEplus project, several CIDOC CRM developers and domain experts have collaborated to undertake conceptual mapping exercises, to address the practicalities of bringing excavation data descriptions together and to link these to our overall aspirations in terms of excavation data discoverability and reusability. In this contribution, we discuss the current state and future directions of the field of semantic representation of archaeological excavation data and consider several issues that constrain the applicability of existing solutions. We identify five key enabling technologies or research areas (Conceptual models and semantic data structures, Conceptual modelling patterns, Data mapping workflows and tools, Learning technologies and Semantic queries) and assign readiness levels to assess their level of technological maturity. Our research demonstrates that while the existing models and domain-specific extensions are deemed adequate, there is a need for more user-friendly methods and tools to structure data in meaningful and interoperable ways. The next steps involve consolidating relevant semantic structures, improving modelling implementation guidance, adhering to consistent workflows, developing engaging curricula, and documenting real-case examples to demonstrate the benefits and results of semantic data integration.
Corresponding author: Markos Katsianis
mkatsianis@upatras.gr
Department of History and Archaeology, University of Patras, GR
George Bruseker
george@takin.solutions
Takin.solutions Ltd
Denitsa Nenova
denitsa@takin.solutions
Takin.solutions Ltd
Olivier Marlet
olivier.marlet@univ-tours.fr
CITERES-LAT, CNRS, University of Tours, FR
Florian Hivert
florian.hivert@univ-tours.fr
MSH Val de Loire, University of Tours, FR
Gerald Hiebel
Gerald.Hiebel@uibk.ac.at
Universität Innsbruck, Archaeology, AT
Christian-Emil Ore
c.e.s.ore@iln.uio.no
University of Oslo, NO
Paola Derudas
paola.derudas@ark.lu.se
Department of Archaeology and Ancient History, Lund University, SE
Rachel Opitz
ropitz@ogc.org
Open Geospatial Consortium
Espen Uleberg
espen.uleberg@khm.uio.no
Museum of Cultural History, University of Oslo, NO
Figure 1: Searching for excavation data items may retrieve results only if the term (referring to a portion of that data collection) is used as a keyword or thematic subject for the entire data collection
Figure 2: The ARIADNE reference model and the complexities of semantic modelling in the excavation domain with respect to retrieval, interpretation and representation of excavated entities
Figure 3: An example of alternative routes to link entities that convey different meanings
Figure 4: Recognition and assignment of base classes and models
Figure 5: Modelling of geospatial data such as coordinates, coordinate systems and projections
Figure 6: Modelling links from excavation activities to the people or groups involved in their performance
Figure 7: Modelling the assignment of chronological statements to archaeological materials
Figure 8: A workflow for RDF creation from spreadsheet data entry to Triple store
Figure 9: A hierarchy transformed into a list of paths
Figure 10: INTRASIS: An object (id:10533) of the subclass 'post hole' of the class 'Archaeological Object' with the attribute 'post function' with the value 'roof supporting', with the attribute 'described at' with the value '2017-07-13' and attribute 'depth' with the value '25 cm'
Figure 11: Workflow used by the MASA Huma-Num consortium to support the publication of archaeological datasets on the semantic Web
Figure 12: Data mapping using the 3M tool and resulting RDF (top). Visualisation of the RDF as a diagram using CRITERIA (bottom)
Figure 13: AIR conceptual data model: a selection of the entities and related semantic classes interconnected through properties. The different models used are indicated by colour. The dotted line indicates the connection with a 'bridging' class that adheres to CIDOC CRM (where in AIR a shortcut was used)
Figure 14: OntoMatchGame: a serious online game to learn ontologies and mapping, shown here featuring a game about the archaeological excavations of Marmoutier Abbey (France)
Figure 15: An example of a cross-dataset query executed in SPARNATURAL using the underlying semantics of Openarchaeo
Table 1: Technology Readiness Levels (TRLs) for key enabling technologies or research areas involved in archaeological excavation modelling
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