Detecting erroneous identity links on the web using network metrics

Joe Raad; Wouter Beek; Frank van Harmelen; Nathalie Pernelle; Fatiha Saïs

doi:10.1007/978-3-030-00671-6_23

Detecting erroneous identity links on the web using network metrics

Joe Raad^*, Wouter Beek, Frank van Harmelen, Nathalie Pernelle, Fatiha Saïs

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

In the absence of a central naming authority on the Semantic Web, it is common for different datasets to refer to the same thing by different IRIs. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Studies that date back as far as 2009, have observed that the owl:sameAs property is sometimes used incorrectly. In this paper, we show how network metrics such as the community structure of the owl:sameAs graph can be used in order to detect such possibly erroneous statements. One benefit of the here presented approach is that it can be applied to the network of owl:sameAs links itself, and does not rely on any additional knowledge. In order to illustrate its ability to scale, the approach is evaluated on the largest collection of identity links to date, containing over 558M owl:sameAs links scraped from the LOD Cloud.

Original language	English
Title of host publication	The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings
Editors	Mari Carmen Suárez-Figueroa, Valentina Presutti, Lucie-Aimee Kaffee, Elena Simperl, Marta Sabou, Denny Vrandecic, Irene Celino, Kalina Bontcheva
Publisher	Springer/Verlag
Pages	391-407
Number of pages	17
ISBN (Print)	9783030006709
DOIs	https://doi.org/10.1007/978-3-030-00671-6_23
Publication status	Published - 2018
Event	17th International Semantic Web Conference, ISWC 2018 - Monterey, United States Duration: 8 Oct 2018 → 12 Oct 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11136 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Semantic Web Conference, ISWC 2018
Country/Territory	United States
City	Monterey
Period	8/10/18 → 12/10/18

Funding

This work was partially conducted within the MaestroGraph project (612.001.553), funded by the Netherlands Organization for Scientific Research (NWO), and was partially supported by the Center for Data Science, funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02. Acknowledgment. This work was partially conducted within the MaestroGraph project (612.001.553), funded by the Netherlands Organization for Scientific Research (NWO), and was partially supported by the Center for Data Science, funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02.

Funders	Funder number
Netherlands Organization for Scientific Research
Agence Nationale de la Recherche
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	612.001.553, ANR-11-IDEX-0003-02

Keywords

Communities
Identity
Linked Open Data
Owl:sameAs

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10.1007/978-3-030-00671-6_23

Raad2018_Chapter_DetectingErroneousIdentityLinkFinal published version, 1.22 MBLicence: Article 25fa Dutch Copyright Act

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Raad, J., Beek, W., van Harmelen, F., Pernelle, N., & Saïs, F. (2018). Detecting erroneous identity links on the web using network metrics. In M. C. Suárez-Figueroa, V. Presutti, L.-A. Kaffee, E. Simperl, M. Sabou, D. Vrandecic, I. Celino, & K. Bontcheva (Eds.), The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings (pp. 391-407). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11136 LNCS). Springer/Verlag. https://doi.org/10.1007/978-3-030-00671-6_23

Raad, Joe ; Beek, Wouter ; van Harmelen, Frank et al. / Detecting erroneous identity links on the web using network metrics. The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings. editor / Mari Carmen Suárez-Figueroa ; Valentina Presutti ; Lucie-Aimee Kaffee ; Elena Simperl ; Marta Sabou ; Denny Vrandecic ; Irene Celino ; Kalina Bontcheva. Springer/Verlag, 2018. pp. 391-407 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In the absence of a central naming authority on the Semantic Web, it is common for different datasets to refer to the same thing by different IRIs. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Studies that date back as far as 2009, have observed that the owl:sameAs property is sometimes used incorrectly. In this paper, we show how network metrics such as the community structure of the owl:sameAs graph can be used in order to detect such possibly erroneous statements. One benefit of the here presented approach is that it can be applied to the network of owl:sameAs links itself, and does not rely on any additional knowledge. In order to illustrate its ability to scale, the approach is evaluated on the largest collection of identity links to date, containing over 558M owl:sameAs links scraped from the LOD Cloud.",

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Raad, J, Beek, W, van Harmelen, F, Pernelle, N & Saïs, F 2018, Detecting erroneous identity links on the web using network metrics. in MC Suárez-Figueroa, V Presutti, L-A Kaffee, E Simperl, M Sabou, D Vrandecic, I Celino & K Bontcheva (eds), The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11136 LNCS, Springer/Verlag, pp. 391-407, 17th International Semantic Web Conference, ISWC 2018, Monterey, United States, 8/10/18. https://doi.org/10.1007/978-3-030-00671-6_23

Detecting erroneous identity links on the web using network metrics. / Raad, Joe; Beek, Wouter; van Harmelen, Frank et al.
The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings. ed. / Mari Carmen Suárez-Figueroa; Valentina Presutti; Lucie-Aimee Kaffee; Elena Simperl; Marta Sabou; Denny Vrandecic; Irene Celino; Kalina Bontcheva. Springer/Verlag, 2018. p. 391-407 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11136 LNCS).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AB - In the absence of a central naming authority on the Semantic Web, it is common for different datasets to refer to the same thing by different IRIs. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Studies that date back as far as 2009, have observed that the owl:sameAs property is sometimes used incorrectly. In this paper, we show how network metrics such as the community structure of the owl:sameAs graph can be used in order to detect such possibly erroneous statements. One benefit of the here presented approach is that it can be applied to the network of owl:sameAs links itself, and does not rely on any additional knowledge. In order to illustrate its ability to scale, the approach is evaluated on the largest collection of identity links to date, containing over 558M owl:sameAs links scraped from the LOD Cloud.

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Raad J, Beek W, van Harmelen F, Pernelle N, Saïs F. Detecting erroneous identity links on the web using network metrics. In Suárez-Figueroa MC, Presutti V, Kaffee LA, Simperl E, Sabou M, Vrandecic D, Celino I, Bontcheva K, editors, The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings. Springer/Verlag. 2018. p. 391-407. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2018 Sept 18. doi: 10.1007/978-3-030-00671-6_23

Detecting erroneous identity links on the web using network metrics

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