Malte Knauf

Learn More
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(More)
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(More)
We present ELLIS, a demo to browse the Linked Data cloud on the level of induced schema patterns. To this end, we define schema-level patterns of RDF types and properties to identify how entities described by type sets are connected by property sets. We show that schema-level patterns can be aggregated and extracted from large Linked Data sets using(More)
In this paper we propose an approach to distinguish affor-dances on a fine-grained scale. We define an anthropomorphic agent model and parameterized affordance models. The agent model is transformed according to affordance parameters to detect affordances in the input data. We present first results on distinguishing two closely related affordances derived(More)
  • 1