State-of-the-art distributed RDF systems partition data across multiple computer nodes
(workers). Some systems perform cheap hash partitioning, which may result in expensive …

Ontologies have been widely used in numerous and varied applications, eg to support data
modeling, information integration, and knowledge management. With the increasing size of …

For reusing an RDF dataset, understanding its content is a prerequisite. To support the
comprehension of its large and complex structure, existing methods mainly generate an …

Recently, user-generated content in social media opened up new alluring possibilities for
understanding the geospatial aspects of many real-world phenomena. Yet, the vast majority …

With the continuous growth of large Knowledge Graphs (KGs), extractive KG summarization
becomes a trending task. Aiming at distilling a compact subgraph with condensed …

The large volume of open data on the Web is expected to be reused and create value.
Finding the right data to reuse is a non-trivial task addressed by the recent dataset search …

Cloud-based systems provide a rich platform for managing large-scale RDF data. However,
the distributed nature of these systems introduces several performance challenges, eg, disk …

Distributed RDF systems partition data across multiple computer nodes. Partitioning is
typically based on heuristics that minimize inter-node communication and it is performed in …

Modern applications require sophisticated analytics on RDF graphs that combine structural
queries with generic graph computations. Existing systems support either declarative …

Distributed RDF data management systems become increasingly important with the growth
of the Semantic Web. Regardless, current methods meet performance bottlenecks either on …