The Linked Open Data (LOD) Cloud has more than tripled its sources in just six
years (from 295 sources in 2011 to 1163 datasets in 2017). The actual Web of Data contains
more then 150 Billions of triples. We are assisting at a staggering growth in the production and
consumption of LOD and the generation of increasingly large datasets. In this scenario, providing
researchers, domain experts, but also businessmen and citizens with visual representations and
intuitive interactions can significantly aid the exploration and understanding of the domains and
knowledge represented by Linked Data.
Various tools and web applications have been developed to enable the navigation, and browsing
of the Web of Data. However, these tools lack in producing high level representations for large
datasets, and in supporting users in the exploration and querying of these big sources. Following this trend, we devised a new method and a tool called H-BOLD (High level visualizations
on Big Open Linked Data). H-BOLD enables the exploratory search and multilevel analysis of
Linked Open Data. It offers different levels of abstraction on Big Linked Data. Through the user
interaction and the dynamic adaptation of the graph representing the dataset, it will be possible
to perform an effective exploration of the dataset, starting from a set of few classes and adding
new ones.
Performance and portability of H-BOLD have been evaluated on the SPARQL endpoint listed
on SPARQL ENDPOINT STATUS. The effectiveness of H-BOLD as a visualization tool is described through a user stud