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Article

Using semantic web resources for data quality management

Authors:

Christian Fürber,

Martin HeppAuthors Info & Claims

EKAW'10: Proceedings of the 17th international conference on Knowledge engineering and management by the masses

Pages 211 - 225

Published: 11 October 2010 Publication History

Abstract

The quality of data is a critical factor for all kinds of decision-making and transaction processing. While there has been a lot of research on data quality in the past two decades, the topic has not yet received sufficient attention from the Semantic Web community. In this paper, we discuss (1) the data quality issues related to the growing amount of data available on the Semantic Web, (2) how data quality problems can be handled within the Semantic Web technology framework, namely using SPARQL on RDF representations, and (3) how Semantic Web reference data, e.g. from DBPedia, can be used to spot incorrect literal values and functional dependency violations. We show how this approach can be used for data quality management of public Semantic Web data and data stored in relational databases in closed settings alike. As part of our work, we developed generic SPARQL queries to identify (1) missing datatype properties or literal values, (2) illegal values, and (3) functional dependency violations. We argue that using Semantic Web datasets reduces the effort for data quality management substantially. As a use-case, we employ Geonames, a publicly available Semantic Web resource for geographical data, as a trusted reference for managing the quality of other data sources.

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Cited By

Bellaaj HMdhaffar AJmaiel MMseddi SFreisleben BHaddad HWainwright RChbeir R(2018)An adaptive neuro-fuzzy inference system for improving data quality in disease registriesProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167376(30-33)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167376
Esnaola-Gonzalez IBermúdez JFernández IFernández SArnaiz A(2017)Towards a Semantic Outlier Detection Framework in Wireless Sensor NetworksProceedings of the 13th International Conference on Semantic Systems10.1145/3132218.3132226(152-159)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3132218.3132226
Scheider SOstermann FAdams B(2017)Why good data analysts need to be critical synthesists. Determining the role of semantics in data analysisFuture Generation Computer Systems10.1016/j.future.2017.02.04672:C(11-22)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.future.2017.02.046
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Information & Contributors

Information

Published In

cover image Guide Proceedings

EKAW'10: Proceedings of the 17th international conference on Knowledge engineering and management by the masses

October 2010

587 pages

ISBN:3642164374

Editors:
Philipp Cimiano
Universität Bielefeld, Cognitive Interaction Technology Excellence Center, Bielefeld, Germany
,
H. Sofia Pinto
IESC-ID, IST, DEI, Lisboa, Portugal

Sponsors

Fundação Calouste Gulbenkian
Talis: Talis
CITEC: The Cognitive Interaction Technology Excellence Cluster
ISOCO: ISOCO
IOS Press: IOS Press

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 October 2010

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Cited By

Bellaaj HMdhaffar AJmaiel MMseddi SFreisleben BHaddad HWainwright RChbeir R(2018)An adaptive neuro-fuzzy inference system for improving data quality in disease registriesProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167376(30-33)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167376
Esnaola-Gonzalez IBermúdez JFernández IFernández SArnaiz A(2017)Towards a Semantic Outlier Detection Framework in Wireless Sensor NetworksProceedings of the 13th International Conference on Semantic Systems10.1145/3132218.3132226(152-159)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3132218.3132226
Scheider SOstermann FAdams B(2017)Why good data analysts need to be critical synthesists. Determining the role of semantics in data analysisFuture Generation Computer Systems10.1016/j.future.2017.02.04672:C(11-22)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.future.2017.02.046
Ristoski PPaulheim H(2016)Semantic Web in data mining and knowledge discoveryWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2016.01.00136:C(1-22)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.websem.2016.01.001
Behkamal BKahani MBagheri E(2015)Quality Metrics for Linked Open DataProceedings, Part I, of the 26th International Conference on Database and Expert Systems Applications - Volume 926110.1007/978-3-319-22849-5_11(144-152)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-22849-5_11
Kontokostas DWestphal PAuer SHellmann SLehmann JCornelissen RZaveri AChung CBroder AShim KSuel T(2014)Test-driven evaluation of linked data qualityProceedings of the 23rd international conference on World wide web10.1145/2566486.2568002(747-758)Online publication date: 7-Apr-2014
https://dl.acm.org/doi/10.1145/2566486.2568002
Meymandpour RDavis JVarde ASuchanek F(2012)Recommendations using linked dataProceedings of the 5th Ph.D. workshop on Information and knowledge10.1145/2389686.2389701(75-82)Online publication date: 2-Nov-2012
https://dl.acm.org/doi/10.1145/2389686.2389701
Baillie CEdwards PPignotti E(2012)Quality assessment, provenance, and the web of linked sensor dataProceedings of the 4th international conference on Provenance and Annotation of Data and Processes10.1007/978-3-642-34222-6_19(220-222)Online publication date: 19-Jun-2012
https://dl.acm.org/doi/10.1007/978-3-642-34222-6_19
Yu YZhang XHeflin JMusen MCorcho O(2011)Learning to detect abnormal semantic web dataProceedings of the sixth international conference on Knowledge capture10.1145/1999676.1999713(177-178)Online publication date: 26-Jun-2011
https://dl.acm.org/doi/10.1145/1999676.1999713
Fürber CHepp M(2011)Towards a vocabulary for data quality management in semantic web architecturesProceedings of the 1st International Workshop on Linked Web Data Management10.1145/1966901.1966903(1-8)Online publication date: 25-Mar-2011
https://dl.acm.org/doi/10.1145/1966901.1966903

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