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.

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

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Recently, object affordances have moved into the focus of researchers in computer vision. Affordances describe how an object can be used by a specific agent. This additional information on the purpose of an object is used to augment the classification process. With the herein proposed approach we aim at bringing affordances and object classification closer together by proposing fine-grained affordances. We present an algorithm that detects fine-grained sitting affordances in point clouds by iteratively transforming a human model into the scene. This approach enables us to distinguish object functionality on a finer-grained scale, thus more closely resembling the different purposes of similar objects. For instance, traditional methods suggest that a stool, chair and armchair all afford sitting. This is also true for our approach, but additionally we distinguish sitting without backrest, with backrest and with armrests. This fine-grained affordance definition closely resembles individual types of sitting and better reflects the purposes of different chairs. We experimentally evaluate our approach and provide fine-grained affordance annotations in a dataset from our lab.

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