Logo des Repositoriums
 
Zeitschriftenartikel

LOD for Library Science: Benefits of Applying Linked Open Data in the Digital Library Setting

Vorschaubild nicht verfügbar

Volltext URI

Dokumententyp

Text/Journal Article

Zusatzinformation

Datum

2016

Zeitschriftentitel

ISSN der Zeitschrift

Bandtitel

Verlag

Springer

Zusammenfassung

Linked Open Data (LOD) has gained widespread adoption by large industries as well as non-profit organizations and governmental organizations. One of the early adopters of LOD technologies are libraries. Since the “early years”, libraries have been key use case and innovation driver for LOD and significantly contributed to the adoption of semantic technologies. The first part of this paper presents selected success stories of current activities in the Linked Data Library community. In a nutshell, these studies include (1) a conceptualization of the Linked Data Value chain, (2) a case study for consumption of Linked Data in a digital journal environment, and (3) an approach to publish metadata on the Semantic Web from an Open Access repository. These stories reveal a strong relationship between LOD in libraries and research topics addressed in traditional fields of computer science such as artificial intelligence, databases, and knowledge discovery. Thus, in the second part of this paper we systematically review the relation of LOD in digital libraries from a computer science perspective. We discuss current LOD research topics such as data integration and schema integration, distributed data management, and others. These challenges have been discussed with computer scientists at a German national database meetup as well as with librarians from ZBW—Leibniz Information Center for Economics and at international librarians meetup.

Beschreibung

Latif, Atif; Scherp, Ansgar; Tochtermann, Klaus (2016): LOD for Library Science: Benefits of Applying Linked Open Data in the Digital Library Setting. KI - Künstliche Intelligenz: Vol. 30, No. 2. Springer. PISSN: 1610-1987. pp. 149-157

Zitierform

DOI

Tags