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Analysis of schema structures in the Linked Open Data graph based on unique subject URIs, pay-level domains, and vocabulary usage

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Abstract

The Linked Open Data (LOD) graph represents a web-scale distributed knowledge graph interlinking information about entities across various domains. A core concept is the lack of pre-defined schema which actually allows for flexibly modelling data from all kinds of domains. However, Linked Data does exhibit schema information in a twofold way: by explicitly attaching RDF types to the entities and implicitly by using domain-specific properties to describe the entities. In this paper, we present and apply different techniques for investigating the schematic information encoded in the LOD graph at different levels of granularity. We investigate different information theoretic properties of so-called Unique Subject URIs (USUs) and measure the correlation between the properties and types that can be observed for USUs on a large-scale semantic graph data set. Our analysis provides insights into the information encoded in the different schema characteristics. Two major findings are that implicit schema information is far more discriminative and that applications involving schema information based on either types or properties alone will only capture between 63.5 and 88.1 % of the schema information contained in the data. As the level of discrimination depends on how data providers model and publish their data, we have conducted in a second step an investigation based on pay-level domains (PLDs) as well as the semantic level of vocabularies. Overall, we observe that most data providers combine up to 10 vocabularies to model their data and that every fifth PLD uses a highly structured schema.

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Notes

  1. http://www.w3.org/DesignIssues/LinkedData.html, accessed: 23 March, 2013.

  2. http://www.w3.org/TR/rdf-syntax-grammar/, accessed: 23 March, 2013.

  3. http://www.lod-cloud.net/, accessed: 23 March, 2013.

  4. Taken from the famous Friend-of-a-Friend (FOAF) vocabulary for describing people and their relations. See: http://www.foaf-project.org/, accessed: 23 March, 2013.

  5. http://challenge.semanticweb.org/, accessed: 23 March, 2013.

  6. Please note, we use the letter \(r\) for sets of properties (inspired by the term relation), as \(p\) will be used to denote probabilities.

  7. Data sources are, e. g., static RDF documents and SPARQL endpoints [17].

  8. BTC 2012 data set: http://km.aifb.kit.edu/projects/btc-2012/, accessed: 25 March, 2013.

  9. Please note, that the efficient stream-based approach can cause an increase in the number of type sets and property sets, as well. This is due to the fact that a single missed type can cause the deduction of a new type set which does not actually occur in the lossless gold standard.

  10. This might not entirely be the case in semi-automated extraction of Linked data (e. g., DBPedia) or when using crowd sourcing for the creation of Linked Data (e. g., Freebase).

  11. http://www.w3.org/TR/2013/CR-vocab-data-cube-20130625/ accessed: 11 September 2013.

  12. https://github.com/cygri/make-void accessed: 21 March, 2013.

  13. http://rdfstats.sourceforge.net/ accessed: 21 March, 2013.

  14. http://www.webdatacommons.org/ accessed: 21 March, 2013.

  15. http://webdatacommons.org/2012-08/stats/how_to_get_the_data.html#toc2 accessed: 21 March, 2013.

  16. http://webdatacommons.org/ accessed: 21 March, 2013.

  17. http://lod-cloud.net/state/ accessed: 21 March, 2013.

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Acknowledgments

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013), REVEAL (Grant agree number 610928). Parts of the computations and experiments were conducted on resources provided by the HPI Future SOC Lab.

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Authors and Affiliations

  1. Institute for Web Science and Technologies (WeST), University of Koblenz-Landau, 56070 , Koblenz, Germany

    Thomas Gottron & Malte Knauf

  2. Kiel University and Leibniz Information Center for Economics, Kiel, Germany

    Ansgar Scherp

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  1. Thomas Gottron
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  2. Malte Knauf
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  3. Ansgar Scherp
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Correspondence to Ansgar Scherp.

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Communicated by Haixun Wang and Jeffrey Xu Yu.

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Gottron, T., Knauf, M. & Scherp, A. Analysis of schema structures in the Linked Open Data graph based on unique subject URIs, pay-level domains, and vocabulary usage. Distrib Parallel Databases 33, 515–553 (2015). https://doi.org/10.1007/s10619-014-7143-0

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