Paper Info
Title
TripleCheckMate: A Tool for Crowdsourcing the Quality Assessment of Linked Data.
Abstract
Linked Open Data (LOD) comprises of an unprecedented volume of structured datasets on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowdsourced and even extracted data of relatively low quality. We present a methodology for assessing the quality of linked data resources, which comprises of a manual and a semi-automatic process. In this paper we focus on the manual process where the first phase includes the detection of common quality problems and their representation in a quality problem taxonomy. The second phase comprises of the evaluation of a large number of individual resources, according to the quality problem taxonomy via crowdsourcing. This process is implemented by the tool TripleCheckMate wherein a user assesses an individual resource and evaluates each fact for correctness. This paper focuses on describing the methodology, quality taxonomy and the tools' system architecture, user perspective and extensibility.
Year
DOI
Venue
2013
10.1007/978-3-642-41360-5_22
Communications in Computer and Information Science
Keywords
Field
DocType
Data Quality,Linked Data,DBpedia
Data science,Data quality,Crowdsourcing,Correctness,Linked data,Systems architecture,Engineering,Extensibility
Conference
Volume
ISSN
Citations 
394
1865-0929
19
PageRank 
References 
Authors
0.79
4
4
Name
Order
Citations
PageRank
Dimitris Kontokostas149031.79
Amrapali Zaveri236824.37
Sören Auer35711418.56
Jens Lehmann45375355.08