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Visualizing the Drift of Linked Open Data Using Self-Organizing Maps
University of Borås, Faculty of Librarianship, Information, Education and IT.ORCID iD: 0000-0002-1539-8256
University of Borås, Faculty of Librarianship, Information, Education and IT.
2016 (English)In: Proceedings of Drift-a-LOD Workshop at the 20th International Conference on Knowledge Engineering and Knowledge Management, 2016Conference paper, Published paper (Refereed)
Abstract [en]

The urge for evolving the Web into a globally shared dataspace has turned the Linked Open Data (LOD) cloud into a massive platform containing 100 billion machine-readable statements. Several factors hamper a historical study of the evolution of the LOD cloud, and hence forecasting its future: its ever-growing scale, which makes a global analysis difficult; its Web-distributed nature, which challenges the analysis of its data; and the scarcity of regular and time-stamped archival dumps. Recently, a scalable implementation of self-organizing maps (SOM) has been developed to visualize the local topology of high-dimensional data. We use this methodology to address scalability issues, and the Dynamic Linked Data Observatory, a regular biweekly, centralized sample of the LOD cloud, as a time-stamped collection. We visualize the drift of Linked Datasets between 2012 and 2016, finding that datasets with high availability, high vocabulary reuse, and modeling with commonly used terms in the LOD cloud are better traceable across time.

Place, publisher, year, edition, pages
2016.
Keywords [en]
Linked Data, Semantic Drift, Visualization
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-11651OAI: oai:DiVA.org:hb-11651DiVA, id: diva2:1062328
Conference
Drift-a-LOD Workshop at the 20th International Conference on Knowledge Engineering and Knowledge Management, Bologna, November 19-23, 2016
Available from: 2017-01-05 Created: 2017-01-05 Last updated: 2018-01-13Bibliographically approved

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Wittek, PeterDarányi, Sándor
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
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  • Other locale
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Output format
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