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Assessing linkset quality for complementing third-party datasets

Authors:

Riccardo Albertoni,

Asunción Gómez PérezAuthors Info & Claims

EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops

Pages 52 - 59

https://doi.org/10.1145/2457317.2457327

Published: 18 March 2013 Publication History

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Abstract

Linked data best practices are getting extremely popular: various companies and public institutions have started taking advantage of linked data principles for exposing their datasets, and for relating their datasets to those served by third parties. Such enthusiasm is due to the linked data promise of evolving into a Global Data Space. Linksets are sets of links relating datasets and they surely play a fundamental role in this promise. However, a stable and well-accepted notion of linkset quality has not been yet defined. This paper contributes to overcome this lack by proposing a linkset quality measure. Among the different quality dimensions that can be addressed, the proposed measure focuses on completeness. The paper formally defines novel scoring functions and proposes an interpretation of these functions when maintaining and complementing third party datasets.

References

[1]

R. Albertoni and M. D. Martino. Semantic similarity and selection of resources published according to linked data best practice. In R. Meersman, T. S. Dillon, and P. Herrero, editors, OTM Workshops, volume 6428 of Lecture Notes in Computer Science, pages 378--383. Springer, 2010.

Digital Library

Google Scholar

[2]

K. Alexander, R. Cyganiak, M. Hausenblas, and J. Zhao. Describing Linked Datasets with the VoID Vocabulary, 2011, http://www.w3.org/TR/2011/NOTE-void-20110303/.

Google Scholar

[3]

C. Bizer and R. Cyganiak. Quality-driven information filtering using the WIQA policy framework. J. Web Sem., 7(1):1--10, 2009.

Digital Library

Google Scholar

[4]

J. Bleiholder and F. Naumann. Data fusion. ACM Comput. Surv., 41(1), 2008.

Digital Library

Google Scholar

[5]

J. Euzenat and P. Shvaiko. Ontology matching. Springer-Verlag, Heidelberg (DE), 2007.

Digital Library

Google Scholar

[6]

A. Flemming and O. Hartig. Quality criteria for linked data. 2010, http://sourceforge.net/apps/mediawiki/trdf/index.php?title=Quality_Criteria_for_Linked_Data_sources

Google Scholar

[7]

C. Guéret, P. T. Groth, C. Stadler, and J. Lehmann. Assessing linked data mappings using network measures. In E. Simperl, P. Cimiano, A. Polleres, Ó. Corcho, and V. Presutti, editors, ESWC, volume 7295 of Lecture Notes in Computer Science, pages 87--102. Springer, 2012.

Digital Library

Google Scholar

[8]

T. Heath and C. Bizer. Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web. Morgan & Claypool Publishers, 2011.

Digital Library

Google Scholar

[9]

P. N. Mendes and C. Bizer. Survey report state of the art in mapping, quality assessment and data fusion. Technical report, LOD2- Creating Knowledge out of Interlinked data, Deliverable 4.3.1, 2011, http://static.lod2.eu/Deliverables/Deliverable_4.3.1_FP7_LOD2_20110131.pdf.

Google Scholar

[10]

P. N. Mendes, C. Bizer, J. H. Young, Z. Miklos, J.-P. Calbimonte, and A. Moraru. Conceptual model and best practices for high-quality metadata publishing. Technical report, PlanetData, Deliverable 2.1, 2012, http://planet-data-wiki.sti2.at/web/File: D2.1.pdf.

Google Scholar

[11]

P. N. Mendes, H. Mühleisen, and C. Bizer. Sieve: linked data quality assessment and fusion. In D. Srivastava and I. Ari, editors, EDBT/ICDT Workshops, pages 116--123. ACM, 2012.

Digital Library

Google Scholar

[12]

L. Pipino, Y. W. Lee, and R. Y. Wang. Data quality assessment. Commun. ACM, 45(4):211--218, 2002.

Digital Library

Google Scholar

[13]

A. Schultz, A. Matteini, R. Isele, C. Bizer, and C. Becker. LDIF - Linked Data Integration Framework. In O. Hartig, A. Harth, and J. Sequeda, editors, COLD, volume 782 of CEUR Workshop Proceedings. CEUR-WS.org, 2011.

Google Scholar

Cited By

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Quarati AAlbertoni R(2024)Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?Future Internet10.3390/fi1603009916:3(99)Online publication date: 15-Mar-2024
https://doi.org/10.3390/fi16030099
Idrissou AZamborlini VKuhn T(2022)Documenting the Creation, Manipulation and Evaluation of Links for Reuse and ReproducibilityKnowledge Engineering and Knowledge Management10.1007/978-3-031-17105-5_6(81-96)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17105-5_6
Issa SAdekunle OHamdi FCherfi SDumontier MZaveri A(2021)Knowledge Graph Completeness: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2021.3056622(1-1)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3056622
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Index Terms

Assessing linkset quality for complementing third-party datasets
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Heterogeneous (hybrid) systems
2. Information systems
  1. Data management systems
  2. Information systems applications

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Published In

EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops

March 2013

423 pages

ISBN:9781450315999

DOI:10.1145/2457317

General Chair:
Giovanna Guerrini
Università di Genova, Italy

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 18 March 2013

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EDBT/ICDT '13

EDBT/ICDT '13: Joint 2013 EDBT/ICDT Conferences

March 18 - 22, 2013

Genoa, Italy

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EDBT '13 Paper Acceptance Rate 7 of 10 submissions, 70%;

Overall Acceptance Rate 7 of 10 submissions, 70%

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Quarati AAlbertoni R(2024)Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?Future Internet10.3390/fi1603009916:3(99)Online publication date: 15-Mar-2024
https://doi.org/10.3390/fi16030099
Idrissou AZamborlini VKuhn T(2022)Documenting the Creation, Manipulation and Evaluation of Links for Reuse and ReproducibilityKnowledge Engineering and Knowledge Management10.1007/978-3-031-17105-5_6(81-96)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17105-5_6
Issa SAdekunle OHamdi FCherfi SDumontier MZaveri A(2021)Knowledge Graph Completeness: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2021.3056622(1-1)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3056622
Albertoni RDe Martino MPodestà P(2018)Quality measures for skosData Technologies and Applications10.1108/DTA-05-2017-003752:3(405-423)Online publication date: 2-Jul-2018
https://doi.org/10.1108/DTA-05-2017-0037
Quarati AAlbertoni RDe Martino M(2017)Overall quality assessment of SKOS thesauriJournal of Information Science10.1177/016555151667107943:6(816-834)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1177/0165551516671079
Valdestilhas ASoru TNgomo ASheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)CEDALProceedings of the International Conference on Web Intelligence10.1145/3106426.3106497(106-113)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106497
Albertoni RMartino MQuarati A(2017)Linked Thesauri Quality Assessment and Documentation for Big Data Discovery2017 International Conference on High Performance Computing & Simulation (HPCS)10.1109/HPCS.2017.16(37-44)Online publication date: Jul-2017
https://doi.org/10.1109/HPCS.2017.16
Sarasua CStaab SThimm M(2017)Methods for Intrinsic Evaluation of Links in the Web of DataThe Semantic Web10.1007/978-3-319-58068-5_5(68-84)Online publication date: 16-May-2017
https://doi.org/10.1007/978-3-319-58068-5_5
Albertoni RDe Martino MPodestà P(2016)Linkset Quality Assessment for the Thesaurus Framework LusTREMetadata and Semantics Research10.1007/978-3-319-49157-8_3(27-39)Online publication date: 4-Nov-2016
https://doi.org/10.1007/978-3-319-49157-8_3
Albertoni RDe Martino MQuarati A(2016)Integrated Quality Assessment of Linked Thesauri for the EnvironmentElectronic Government and the Information Systems Perspective10.1007/978-3-319-44159-7_16(221-235)Online publication date: 7-Aug-2016
https://doi.org/10.1007/978-3-319-44159-7_16
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Test-driven evaluation of linked data quality

Linked Data Quality Assessment: A Survey

Assessing quality score of Wikipedia article using mutual evaluation of editors and texts

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