Classifying the LOD cloud: Digging into the knowledge graph

Daniel Martínez Ávila; Richard P. Smiraglia; Rick Szostak; Andrea Scharnhorst; Wouter Beek; Ronald Siebes; Laura Ridenour; Vanessa Schlais

doi:10.36311/1981-1640.2018.v12n4.02.p6

Authors

Daniel Martínez Ávila Universidade Estadual Paulista https://orcid.org/0000-0003-2236-553X
Richard P. Smiraglia School of Information Studies, University of Wisconsin-Milwaukee (USA) https://orcid.org/0000-0003-0724-1926
Rick Szostak University of Alberta https://orcid.org/0000-0001-8570-4418
Andrea Scharnhorst Data Archiving and Networked Services, Royal Netherlands Academy of the Arts and Sciences, The Hague, The Netherlands) https://orcid.org/0000-0001-8879-8798
Wouter Beek Vrije Universiteit (Amsterdam, The Netherlands) https://orcid.org/0000-0003-0250-9655
Ronald Siebes Data Archiving and Networked Services, Royal Netherlands Academy of the Arts and Sciences, The Hague, The Netherlands) https://orcid.org/0000-0001-8772-7904
Laura Ridenour School of Information Studies, University of Wisconsin-Milwaukee (USA) https://orcid.org/0000-0003-0460-789X
Vanessa Schlais School of Information Studies, University of Wisconsin-Milwaukee (USA) https://orcid.org/0000-0001-7690-4389

DOI:

https://doi.org/10.36311/1981-1640.2018.v12n4.02.p6

Keywords:

Linked Open Data, Knowledge Organisation Systems, Big Data, Knowledge Graph

Abstract

Massive amounts of data from different contexts and producers are collected and connected relying often solely on statistical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project “Digging Into the Knowledge Graph,” funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminology employed within the LOD cloud with terminology employed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in computer science, and the KO community, with members from linguistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communities.

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Author Biography

Richard P. Smiraglia, School of Information Studies, University of Wisconsin-Milwaukee (USA)

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