There has been a recent, tangible growth in RDF published on the Web in accordance with
the Linked Data principles and best practices, the result of which has been dubbed the “Web …

Resource Description Framework (RDF) can seen as a solution in today's landscape of
knowledge representation research. An RDF language has symmetrical features because …

When working with large amounts of crawled semantic data as provided by the Billion Triple
Challenge (BTC), it is desirable to present the data in a manner best suited for end users …

We explore the design and implementation of a scalable Datalog system using Hadoop as
the underlying runtime system. Observing that several successful projects provide a …

Crunching large graphs is the basis of many emerging applications, such as social network
analysis and bioinformatics. Graph analytics algorithms exhibit little locality and therefore …

In recent years the web has evolved from a global information space of linked documents to
one where both documents and data are linked. What supports this evolution is a set of best …

Abstract Knowledge Discovery (KD) is a long-tradition field aiming at developing
methodologies to detect hidden patterns and regularities in large datasets, using techniques …

As semantic datasets grow to be very large and divergent, there is a need to identify and
exploit their inherent semantic structure for discovery and optimization. Towards that end, we …

Given a semantic graph data set, perhaps one lacking in an explicit ontology, we wish to first
identify its significant semantic structures, and then measure the extent of their significance …

As semantic graph database technology grows to address components ranging from large
triple stores to SPARQL endpoints over SQL-structured relational databases, it will become …