Vol. 14 No. 3 (2023)

Archival Finding Aids in Linked Open Data between description and interpretation

Francesca Tomasi
Alma Mater Studiorum University of Bologna

Published 2023-09-15


  • Semantic Web,
  • Digital Hermeneutics,
  • Interpretation,
  • Trustworthiness,
  • Digital Humanities.

How to Cite

Tomasi, Francesca. 2023. “Archival Finding Aids in Linked Open Data Between Description and Interpretation”. JLIS.It 14 (3):134-46. https://doi.org/10.36253/jlis.it-557.


The Semantic Web in general, and the LOD in particular, suppose that the knowledge conveyed by documents must be adequately modeled and represented to produce reliable and trustworthy data. Following this statement, we understand that in the archival domain the tricky and subtle transition from the traditional methodologies for data description to Linked Open Data must be delegated to agents able to skilfully read the content of cultural objects. The Digital Hermeneutics model aims to propose a layered architecture that allows, beyond the descriptive specificities of each domain, to formalize the data transformation from the native system to LOD. The idea is to guarantee, through context information, that each moment of the transformation workflow is documented, finally strengthening the trust of the resultant dataset.


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  1. Barabucci, Gioele, Francesca Tomasi, e Fabio Vitali. 2022. “Modeling data complexity in public history and cultural heritage.” In Handbook of Digital Public History, 459-74. Oldenbourg: De Gruyter. DOI: https://doi.org/10.1515/9783110430295-041
  2. Carroll, Jeremy J., Christian Bizer, Pat Hayes, e Patrick Stickler. 2005. “Named graphs, provenan- ce and trust.” In Proceedings of the 14th International Conference on World Wide Web, 613–22. New York: ACM. https://doi.org/10.1145/1060745.1060835. DOI: https://doi.org/10.1145/1060745.1060835
  3. Ceolin, Davide, Paul Groth, Valentina Maccatrozzo, Wan Fokkink, Willem Robert Van Hage, e Archana Nottamkandath. 2016. “Combining user reputation and provenance analysis for trust assessment.” Journal of Data and Information Quality (JDIQ) 7 (1-2):1–28. https://doi. org/10.1145/2818382. DOI: https://doi.org/10.1145/2818382
  4. Daquino, Marilena, e Francesca Tomasi. 2015. “Historical Context Ontology (HiCO): A Concep- tual Model for Describing Context Information of Cultural Heritage Objects. “ In Metadata and Semantics Research, a cura di Emmanouel Garoufallou, Richard J. Hartley, e Panorea Gaitanou, 544:424–36. Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-24129-6_37
  5. Daquino, Marilena, Francesca Giovannetti, e Francesca Tomasi. 2019. “Linked data ed edizioni scientifiche digitali. Esperimenti di trasformazione di un Quaderno di appunti.” Umanistica Digi- tale 3(7). https://doi.org/10.6092/issn.2532-8816/9091.
  6. Daquino, Marilena, Valentina Pasqual, e Francesca Tomasi. 2020. “Knowledge representation of digital hermeneutics of archival and literary sources.” JLIS.it 11 (3):59-76. https://doi.org/10.4403/ jlis.it-12642.
  7. Daquino, Marilena, Mari Wigham, Enrico Daga, Lucia Giagnolini, e Francesca Tomasi. 2022. “CLEF. A Linked Open Data native system for Crowdsourcing.” https://arxiv.org/abs/2206.08259.
  8. Groth, Paul, Andrew Gibson, e Jan Velterop. 2010. “The Anatomy of a Nanopublication.” Infor- mation Services & Use 30 (1–2):51–56. https://doi.org/10.3233/ISU-2010-0613. DOI: https://doi.org/10.3233/ISU-2010-0613
  9. Moreau, Luc, Paul Groth, James Cheney, Timothy Lebo, e Simon Miles. 2015. “The Rationale of PROV.” Web Semantics: Science, Services and Agents on the World Wide Web 35:235–57. https://doi. org/10.1016/j.websem.2015.04.001. DOI: https://doi.org/10.1016/j.websem.2015.04.001
  10. Pasqual, Valentina e Francesca Tomasi. 2022. “Linked Open Data per la valorizzazione di collezio- ni culturali: il dataset mythLOD.” AIB Studi 62 (1):149-68. https://doi.org/10.2426/aibstudi-13301.
  11. Sandusky, Robert J. 2016. “Computational provenance: DataONE and implications for cultural heritage institutions.” In 2016 IEEE International Conference on Big Data, 3266–71. https://doi. org/10.1109/BigData.2016.7840984. DOI: https://doi.org/10.1109/BigData.2016.7840984
  12. Sikos, Leslie F., e Dean Philp. 2020. “Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs.” Data Science and Engineering 5 (3):293–316. https://doi.org/10.1007/s41019-020-00118-0. DOI: https://doi.org/10.1007/s41019-020-00118-0
  13. Tomasi, Francesca. 2022. Organizzare la conoscenza. Digital Humanities e Web Semantico. Milano: Editrice Bibliografica.