Abstract
Datasets published on the Web and following the Linked Open Data (LOD) practices have the potential to enrich other LOD datasets in multiple domains. However, the lack of descriptive information, combined with the large number of available LOD datasets, inhibits their interlinking and consumption. Aiming at facilitating such tasks, this paper proposes an automated clustering process for the LOD datasets that, thereby, provide an up-to-date description of the LOD cloud. The process combines metadata inspection and extraction strategies, community detection methods and dataset profiling techniques. The clustering process is evaluated using the LOD diagram as ground truth. The results show the ability of the proposed process to replicate the LOD diagram and to identify new LOD dataset clusters. Finally, experiments conducted by LOD experts indicate that the clustering process generates dataset clusters that tend to be more descriptive than those manually defined in the LOD diagram.
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Acknowledgments
This work was partly funded by CNPq under grants 153908/2015-7, 557128/2009-9, 444976/2014-0, 303332/2013-1, 442338/2014-7 and 248743/2013-9 and by FAPERJ under grants e E-26-170028/2008 and E-26/201.337/2014. The authors would also like to thank the Microsoft Azure Research Program by the cloud resources awarded for the project entitled “Assessing Recommendation Approaches for Dataset Interlinking”.
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Caraballo, A.A.M., Nunes, B.P., Lopes, G.R., Leme, L.A.P.P., Casanova, M.A. (2016). Automatic Creation and Analysis of a Linked Data Cloud Diagram. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_31
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