Matches in ScholarlyData for { ?s ?p 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 analyse the information theoretic proper- ties and the correlation between the two manifestations of schema information. To this end, we have extracted schema information regarding the types and prop- erties defined in the datasets 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 in- formation is far more discriminative and that a schema based on either types or properties alone will only capture between 63.5% and 88.1% of the schema infor- mation contained in the data. Based on these observations, we derive conclusions about the design of future schemas for LOD as well as potential application sce- narios.. }
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- 68 abstract "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 analyse the information theoretic proper- ties and the correlation between the two manifestations of schema information. To this end, we have extracted schema information regarding the types and prop- erties defined in the datasets 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 in- formation is far more discriminative and that a schema based on either types or properties alone will only capture between 63.5% and 88.1% of the schema infor- mation contained in the data. Based on these observations, we derive conclusions about the design of future schemas for LOD as well as potential application sce- narios.".