Published April 22, 2022 | Version 1.2
Standard Open

CRMhs Ontology - A CIDOC CRM extension for the semantic modelling of Heritage Science information

Authors/Creators

  • 1. Fondazione PIN - Polo di Prato dell'Università di Firenze

Description

The CRMhs (Heritage Science) ontology is a formal semantic model designed to capture and represent the full complexity of scientific investigations applied to cultural heritage. It provides a structured conceptual framework for the documentation, preservation, and integration of heterogeneous research information, ranging from experimental and preparatory actions to interpretative syntheses. By establishing shared semantic definitions, CRMhs enables the transformation of fragmented, highly contextualized datasets into a semantically interconnected global knowledge space, facilitating data interoperability and long-term reuse across disciplinary and institutional boundaries.

The ontology uses and extends the CIDOC Conceptual Reference Model (CIDOC CRM, ISO 21127:2014), specifically version 7.1.3, as its conceptual foundation. It also aligns with specialized extensions such as CRMsci (for scientific observation), CRMdig (for digital provenance), and CRMinf (for argumentation and belief adoption). This alignment ensures that scientific investigation data is seamlessly integrated with the broader cultural and historical records of heritage objects, maintaining a unified conceptual space for both humanistic and scientific inquiry.

The model is structured around several core pillars of Heritage Science research:

  • Scientific Activities: Represented by the top-level class HS1 Scientific Activity, the model tracks the execution of complex workflows, including sample preparation (HS10), analytical measurements (HS3), and data calibration.
  • Material and Digital Entities: CRMhs provides fine-grained modeling for physical objects intentionally selected for study (HS6 Study Object), extracted fragments (HS9 Study Object Sample), and localized surface areas (HS8 Study Object Area), as well as their digital outputs, such as HS13 Scientific Datasets.
  • Technical Context: It captures the essential technical parameters of research, including the specialized instrumentation used (HS11 Scientific Device), individual hardware components (HS12), and the precise HS14 Analysis Settings (e.g., voltage, intensity, or scanning velocity) that ensure experimental reproducibility.

CRMhs is developed to support major European research infrastructures and initiatives, such as ARIADNE and ARTEMIS, and adheres to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). It serves as a vital tool for cultural heritage institutions and laboratories to organize their digital archives, allowing new research questions to be addressed through the re-evaluation of existing datasets without the need for invasive or costly repeat measurements.

The complete documentation, the RDFS encoding, and usage examples of the CRMhs model are available in the official ontology repository on GitHub.

Files

CRMhs_Ontology_Specification_v1.2.pdf

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Additional details

Dates

Created
2018
Updated
2022
Version 1.0
Updated
2024
Version 1.1
Issued
2026-03-11
Version 1.2
Updated
2026-04-22
Version 1.2 (revision)

Software

Repository URL
https://github.com/vastlab-dev/CRMhs
Development Status
Active

References

  • Niccolucci, F., & Felicetti, A. (2018). A CIDOC CRM-based model for the documentation of heritage sciences. In 2018 3rd Digital Heritage International Congress (DigitalHERITAGE) held jointly with 2018 24th International Conference on Virtual Systems & Multimedia (VSMM 2018) (pp. 1–6). IEEE. https://doi.org/10.1109/DigitalHeritage.2018.8810109.
  • Castelli, L., Felicetti, A., & Proietti, F. (2021). Heritage science and cultural heritage: Standards and tools for establishing cross-domain data interoperability. International Journal on Digital Libraries, 22(3), 279–287. https://doi.org/10.1007/s00799-019-00275-2.
  • Bombini, A., Castelli, L., dell'Agnello, L., Felicetti, A., Giacomini, F., Niccolucci, F. and Taccetti, F. (2021). CHNet cloud: an EOSC-based cloud for physical technologies applied to cultural heritages. In GARR (ed.) Conferenza GARR. https://doi.org/10.26314/GARR-Conf21- proceedings-09.
  • Bombini, A., Alkhansa, A., Cappelli, L., Felicetti, A., Giacomini, F., & Costantini, A. (2022). A Cloud-Native Web Application for Assisted Metadata Generation and Retrieval: THESPIAN- NER. Applied Sciences, 12(24), 12910. https://doi.org/10.3390/app122412910.
  • Bombini, A., Castelli, L., Felicetti, A., Niccolucci, F., Reccia, A., & Taccetti, F. (2022). Towards the creation of AI-powered queries using transfer learning on NLP model - The THESPIAN-NER experience. In P. L. Mazzeo, E. Frontoni, S. Sclaroff, & C. Distante (eds.), Image Analysis and Processing - ICIAP 2022 Workshops (Vol. 13374, pp. 257–268). Springer. https://doi.org/10.1007/978-3-031-13324-4_23.
  • Felicetti, A., Himmiche, A., & Somenzi, M. (2025). Knowledge Graphs and Artificial Intelligence for the Implementation of Cognitive Heritage Digital Twins. Applied Sciences, 15(18), 10061. https://doi.org/10.3390/app151810061.
  • Felicetti, A., & Murano, F. (2026). Bridging "Nature" and "Spirit": The CRMhs Ontology for the Integration of Heritage Science and Cultural Heritage Data. Preprints. https://doi.org/10.20944/preprints202603.2289.v1.