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Distributed and Self-organizing Systems
Distributed and Self-organizing Systems
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PUBLICATION

SemQuire - Assessing the Data Quality of Linked Open Data Sources.

Type

Conference Paper

Year

2018

Authors

andrelanger

siegert

christophgoepfert

gaedke

Research Area

Intelligent Information Management

Event

1st International Workshop on Engineering Open Data (WEOD)

Published in

Proceedings of 18th International Conference on Web Engineering (ICWE2018)

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Abstract

The  World  Wide  Web  represents  a  tremendous  source  of knowledge, whose amount constantly increases. Open Data initiatives and the Semantic Web community have emphasized the need to publish data in a structured format based on open standards and ideally linked to other data sources. But that does not necessarily lead to error-free information and data of good quality. It would be of high relevance to have a software component that is capable of measuring the most relevant quality metrics in a generic fashion as well as rating these results. We therefore present SemQuire, a quality assessment tool for analyzing quality  aspects  of  particular  Linked  Data  sources  both  in  the  Open Data  context  as  well  as  in  the  Enterprise  Data  Service  context.  It  is based on open standards such as W3C’s RDF, SPARQL and DQV, and implements as a proof-of-concept a basic set of 55 recommended intrinsic, representational, contextual and accessibility quality metrics. We provide a use case for evaluating SemQuire’s feasibility and effectiveness.

Reference

Langer, André; Siegert, Valentin; Göpfert, Christoph; Gaedke, Martin: SemQuire - Assessing the Data Quality of Linked Open Data Sources.. Proceedings of 18th International Conference on Web Engineering (ICWE2018), 2018.





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