Linked Data Quality Assessment and its Application to Societal Progress Measurement

Open Access
Author:
Description:
In recent years, the Linked Data (LD) paradigm has emerged as a simple mechanism for employing the Web as a medium for data and knowledge integration where both documents and data are linked. Moreover, the semantics and structure of the underlying data are kept intact, making this the Semantic Web. LD essentially entails a set of best practices for publishing and connecting structure data on the Web, which allows publish- ing and exchanging information in an interoperable and reusable fashion. Many different communities on the Internet such as geographic, media, life sciences and government have already adopted these LD principles. This is confirmed by the dramatically growing Linked Data Web, where currently more than 50 billion facts are represented. With the emergence of Web of Linked Data, there are several use cases, which are possible due to the rich and disparate data integrated into one global information space. Linked Data, in these cases, not only assists in building mashups by interlinking heterogeneous and dispersed data from multiple sources but also empowers the uncovering of meaningful and impactful relationships. These discoveries have paved the way for scientists to explore the existing data and uncover meaningful outcomes that they might not have been aware of previously. In all these use cases utilizing LD, one crippling problem is the underlying data quality. Incomplete, inconsistent or inaccurate data affects the end results gravely, thus making them unreliable. Data quality is commonly conceived as fitness for use, be it for a certain application or use case. There are cases when datasets that contain quality problems, are useful for certain applications, thus depending on the use case at hand. Thus, LD consumption has to deal with the problem of getting the data into a state in which it can be exploited for real use cases. The insufficient data quality can be caused either by the LD publication process or is intrinsic to the data source itself. A key challenge is to assess the quality of ...
Contributors:
Naumann, Felix ; Universität Leipzig
Year of Publication:
2015-05-19
Document Type:
doc-type:doctoralThesis ; info:eu-repo/semantics/doctoralThesis ; doc-type:Text ; [Doctoral and postdoctoral thesis]
Language:
eng
Subjects:
info:eu-repo/classification/ddc/500 ; ddc:500 ; Linked Data ; Datenqualität ; Semantic Web ; Data Quality
DDC:
500 Natural sciences & mathematics ; 330 Economics (computed) ; 004 Data processing & computer science (computed)
Rights:
info:eu-repo/semantics/openAccess
Relations:
URL:
https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa-167021
https://ul.qucosa.de/id/qucosa%3A13295
https://ul.qucosa.de/api/qucosa%3A13295/attachment/ATT-0/
Content Provider:
Universität Leipzig: Qucosa  Flag of Germany