Both researchers and industry are confronted with the need to process increasingly large
amounts of data, much of which has a natural graph representation. Some use MapReduce for …

The topics of data analytics and querying in the context of Knowledge Graphs have been
addressed as part of two separate fields. However, most data processing pipelines using …

Summarizing graphs wrt structural features is important to reduce the graph’s size and make
tasks like indexing, querying, and visualization feasible. Our generic parallel BRS algorithm …

Works on knowledge graphs and graph-based data management often focus either on graph
query languages or on frameworks for graph analytics, where there has been little work in …

Graph summarization is the task of finding condensed representations of graphs such that a
chosen set of (structural) subgraph features in the graph summary are equivalent to the …

Automated computer-aided diagnosis (CAD) is becoming more significant in the field of
medicine due to advancements in computer hardware performance and the progress of artificial …

Work on knowledge graphs and graph-based data management often focus either on
declarative graph query languages or on frameworks for graph analytics, where there has been …

We developed a flexible parallel algorithm for graph summarization based on vertex-centric
programming and parameterized message passing. The base algorithm supports infinitely …

Background In today’s landscape of data management, the importance of knowledge
graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding …

In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …