A Systematic Investigation of Explicit and Implicit Schema Information on the Linked Open Data Cloud

Schema information about resources in the Linked Open Data (LOD) cloud can be provided in a twofold way: it can be explicitly defined by attaching RDF types to the resources. Or it is provided implicitly via the definition of the resources’ properties. In this paper, we present a method and metrics to analyse the information theoretic properties and the correlation between the two manifestations of schema information. Furthermore, we actually perform such an analysis on large-scale linked data sets. To this end, we have extracted schema information regarding the types and properties defined in the data set segments provided for the Billion Triples Challenge 2012. We have conducted an in depth analysis and have computed various entropy measures as well as the mutual information encoded in the two types of schema information. 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. Based on these observations, we derive conclusions about the design of future schemas for LOD as well as potential application scenarios.