Semantic Data-Modeling and Long-Term Interpretability of Cultural Heritage Data - Three Case Studies

Wagner S, Albers L, Große P (2019)

Publication Language: English

Publication Type: Book chapter / Article in edited volumes

Publication year: 2019

Publisher: Springer

Edited Volumes: Digital Cultural Heritage

DOI: 10.1007/978-3-030-15200-0_16

Abstract

Research institutions and museums are increasingly initiating projects where large amounts of metadata are collected that describe both the content and the context of the collected data. The use of semantic methods enables their connection. In addition, these data must remain interpretable by man and machine even after the end of funded research projects. Therefore, the data-model used has to enable a mapping of the content and context of the respective research project as well as to develop the digital resources on a sustainable basis. In the following, three different projects with diverse topics are presented, in which domain knowledge is modeled on the basis of an ontology, in this case the CIDOC Conceptual Reference Model (CRM).

Authors with CRIS profile

Sarah Wagner FAU Competence Center Research Data and Information (FAU CDI) Laura Albers FAU Competence Center Research Data and Information (FAU CDI) Peggy Große Lehrstuhl für Kunstgeschichte

How to cite

APA:

Wagner, S., Albers, L., & Große, P. (2019). Semantic Data-Modeling and Long-Term Interpretability of Cultural Heritage Data - Three Case Studies. In Kremers, Horst (Eds.), Digital Cultural Heritage. Springer.

MLA:

Wagner, Sarah, Laura Albers, and Peggy Große. "Semantic Data-Modeling and Long-Term Interpretability of Cultural Heritage Data - Three Case Studies." Digital Cultural Heritage. Ed. Kremers, Horst, Springer, 2019.

BibTeX: Download