There is a vast amount of Linked Data on the web spread
across a large number of datasets. One of the visions behind
Linked Data is that the published data is conveniently
reusable by others. This, however, depends on many details
such as conformance of the data with commonly used vocabularies
and adherence to best practices for data modeling.
Therefore, when an expert wants to reuse existing datasets,
he still needs to analyze them to discover how the data is
modeled and what it actually contains. This may include
analysis of what entities are there, how are they linked to
other entities, which properties from which vocabularies are
used, etc. What is missing is a convenient and fast way of
seeing what could be usable in the chosen unknown dataset
without reading through its RDF serialization. In this paper
we describe use cases based on this problem and their realization
using our Linked Data Visualization Model (LDVM)
and its new implementation. LDVM is a formal base that
exploits the Linked Data principles to ensure interoperability
and compatibility of compliant analytic and visualization
components. We demonstrate the use cases on examples
from the Czech Linked Open Data cloud.