Remodeling archival metadata descriptions for linked archives

Brian Dobreski, Jaihyun Park, Alicia Leathers, Jian Qin

Research output: Contribution to journalConference Articlepeer-review

3 Scopus citations


Though archival resources may be valued for their uniqueness, they do not exist in isolation from each other, and stand to benefit from linked data treatments capable of exposing them to a wider network of resources and potential users. To leverage these benefits, existing, item-level metadata depicting physical materials and their digitized surrogates must be remodeled as linked data. A number of solutions exist, but many current models in this domain are complex and may not capture all relevant aspects of larger, heterogeneous collections of media materials. This paper presents the development of the Linked Archives model, a linked data approach to making item-level metadata available for archival collections of media materials, including photographs, sound recordings, and video recordings. Developed and refined through an examination of existing collection and item metadata alongside comparisons to established domain ontologies and vocabularies, this model takes a modular approach to remodeling archival data as linked data. Current efforts focused on a simplified, user discovery focused module intended to improve access to these materials and the incorporation of their metadata into the wider web of data. This project contributes to work exploring the representation of the range of archival and special collections and how these materials may be addressed via linked data models.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalProceedings of the International Conference on Dublin Core and Metadata Applications
StatePublished - 2019
Event9th Dublin Core Metadata Initiative International Conference on Dublin Core and Metadata Applications, DCMI 2019 - Seoul, Korea, Republic of
Duration: Sep 23 2019Sep 26 2019


  • Archives metadata
  • Item-level metadata
  • Linked Archives
  • Linked data
  • Ontological modeling

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software


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