Finding the Achilles Heel of the Web of Data: Using Network Analysis for Link-Recommendation

The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, e-commerce and science. As a result, the robustness of this Web of Data is now crucial. Prior studies show that the Web of Data is strongly dependent on a small number of centr...

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Bibliographic Details
Published inThe Semantic Web – ISWC 2010 pp. 289 - 304
Main Authors Guéret, Christophe, Groth, Paul, van Harmelen, Frank, Schlobach, Stefan
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
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Summary:The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, e-commerce and science. As a result, the robustness of this Web of Data is now crucial. Prior studies show that the Web of Data 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 apply these on a substantial subset of it, the 2010 Billion Triple Challenge dataset. We first distinguish the physical structure of the Web of Data from its semantic structure. For both of these structures, we then calculate their robustness, taking betweenness centrality as a robustness-measure. To the best of our knowledge, this is the first time that such robustness-indicators have been calculated for the Web of Data. Finally, we determine which links should be added to the Web of Data in order to improve its robustness most effectively. 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. We believe that with this work, we offer an effective method to analyse and improve the most important structure that the Semantic Web community has constructed to date.
ISBN:364217745X
9783642177453
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-17746-0_19