Exposing library holdings metadata in RDF using schema.org semantics

Research output: Contribution to journalConference article

Abstract

Libraries have been busy transforming and publishing their data as linked open data by testing already existing semantics and developing new sets of semantics. So far, most of the efforts have focused on the bibliographic data, not the holdings and item related data that are unique to individual libraries and that help users access the information resources they need. The University of Illinois at Urbana-Champaign Library experimented with a subset of its bibliographic records (5.4 million) describing print resources and associated holdings data to examine options and best practices so far identified for expressing library holdings data using schema.org semantics. The experimentation suggests that the mappings for holdings data recommended by the BibExtend Community Group are in some ways incomplete and that some proposed uses of schema.org types and properties to describe library holdings go beyond current schema.org definitions. Existing schema.org enumerations should be extended (e.g., regarding availability) to better describe library use cases, and some extensions to schema.org are needed to fully describe library holdings data and to maximize their utility. This paper highlights issues, suggests potential extensions identified during the transformation to schema.org semantics, and discusses options to make essential library holdings data fully visible as linked open data.

Original languageEnglish (US)
Pages (from-to)41-49
Number of pages9
JournalProceedings of the International Conference on Dublin Core and Metadata Applications
StatePublished - Jan 1 2015
Event15th 2015 International Conference on Dublin Core and Metadata Applications, DCMI 2015 - Sao Paulo, Brazil
Duration: Sep 1 2015Sep 4 2015

Keywords

  • Holding data
  • Library catalog
  • Linked open data
  • Schema,org
  • Semantic Web

ASJC Scopus subject areas

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

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