Classifications as Linked Open Data: Challenges and Opportunities

Rick Szostak, Richard P. Smiraglia, Andrea Scharnhorst, Ronald Siebes, Aida Slavic, Daniel Martínez Ávila, Tobias Renwick

Research output: Chapter in book/volumeChapterScientificpeer-review

Abstract

Linked Data (LD) as a web-based technology enables in principle the seamless, machine-supported integration, interplay and augmention of all kinds of knowledge, into what has been labeled a huge knowledge graph. Despite decades of web technology and, more recently, the Linked Data approach, the task to fully exploit these new technologies in the public domain is only commencing. One specific challenge is to transfer techniques developed pre-web to order our knowledge into the realm of Linked (Open) Data. This paper illustrates two different models in which a general analytico-synthetic classification can be published and made available as linked data. In both cases, a linked data solution deals with the intricacies of a pre-coordinated indexing language: the Universal Decimal Classification (UDC) and the Basic Concepts Classification (BCC).
Original languageEnglish
Title of host publicationKnowledge Organization at the Interface
Subtitle of host publicationProceedings of the Sixteenth International ISKO Conference, 2020 Aalborg, Denmark
EditorsMarianne Lykke, Tanja Svarre, Daniel Martínez-Ávila
Pages436-455
Edition2020
ISBN (Electronic)978-3-95650-776-2
DOIs
Publication statusPublished - 2020

Publication series

NameAdvances in Knowledge Organization
Volume17

Keywords

  • Classifications
  • Linked Open Data

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