Finding the achilles heel of the web of data using network analysis tools

Christophe Guéret; Paul Groth; Frank van Harmelen; Stefan Schlobach

Finding the achilles heel of the web of data using network analysis tools

Christophe Guéret, Paul Groth, Frank van Harmelen, Stefan Schlobach

Research output: Contribution to journal › Conference article › peer-review

Abstract

The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, e-commerce and science. The robustness of this Web of Data is now crucial. Prior studies show that this Web is strongly dependent on a small number of central hubs, making it highly vulnerable to single points of failure. In this paper, we present concepts and algorithms to analyse and repair the brittleness of the Web of Data. We take the betweenness centrality as a robustness-measure and determine which links should be added to theWeb of Data to decrease the centrality index of the network. We are able to determine such links by interpreting the question as a very large optimisation problem and deploying an evolutionary algorithm to solve this problem.

Original language	English
Journal	Belgian/Netherlands Artificial Intelligence Conference
State	Published - 2011
Externally published	Yes
Event	23rd Benelux Conference on Artificial Intelligence, BNAIC 2011 - Ghent, Belgium Duration: Nov 3 2011 → Nov 4 2011

Cite this

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title = "Finding the achilles heel of the web of data using network analysis tools",

abstract = "The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, e-commerce and science. The robustness of this Web of Data is now crucial. Prior studies show that this Web is strongly dependent on a small number of central hubs, making it highly vulnerable to single points of failure. In this paper, we present concepts and algorithms to analyse and repair the brittleness of the Web of Data. We take the betweenness centrality as a robustness-measure and determine which links should be added to theWeb of Data to decrease the centrality index of the network. We are able to determine such links by interpreting the question as a very large optimisation problem and deploying an evolutionary algorithm to solve this problem.",

author = "Christophe Gu{\'e}ret and Paul Groth and {van Harmelen}, Frank and Stefan Schlobach",

year = "2011",

language = "Ingl{\'e}s",

journal = "Belgian/Netherlands Artificial Intelligence Conference",

issn = "1568-7805",

note = "23rd Benelux Conference on Artificial Intelligence, BNAIC 2011 ; Conference date: 03-11-2011 Through 04-11-2011",

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T1 - Finding the achilles heel of the web of data using network analysis tools

AU - Guéret, Christophe

AU - Groth, Paul

AU - van Harmelen, Frank

AU - Schlobach, Stefan

PY - 2011

Y1 - 2011

N2 - The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, e-commerce and science. The robustness of this Web of Data is now crucial. Prior studies show that this Web is strongly dependent on a small number of central hubs, making it highly vulnerable to single points of failure. In this paper, we present concepts and algorithms to analyse and repair the brittleness of the Web of Data. We take the betweenness centrality as a robustness-measure and determine which links should be added to theWeb of Data to decrease the centrality index of the network. We are able to determine such links by interpreting the question as a very large optimisation problem and deploying an evolutionary algorithm to solve this problem.

AB - The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, e-commerce and science. The robustness of this Web of Data is now crucial. Prior studies show that this Web is strongly dependent on a small number of central hubs, making it highly vulnerable to single points of failure. In this paper, we present concepts and algorithms to analyse and repair the brittleness of the Web of Data. We take the betweenness centrality as a robustness-measure and determine which links should be added to theWeb of Data to decrease the centrality index of the network. We are able to determine such links by interpreting the question as a very large optimisation problem and deploying an evolutionary algorithm to solve this problem.

UR - http://www.scopus.com/inward/record.url?scp=84874013946&partnerID=8YFLogxK

M3 - Artículo de la conferencia

AN - SCOPUS:84874013946

SN - 1568-7805

JO - Belgian/Netherlands Artificial Intelligence Conference

JF - Belgian/Netherlands Artificial Intelligence Conference

T2 - 23rd Benelux Conference on Artificial Intelligence, BNAIC 2011

Y2 - 3 November 2011 through 4 November 2011

ER -

Finding the achilles heel of the web of data using network analysis tools

Abstract

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