Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten
years. Building on a storied tradition of graphs in the AI community, a KG may be simply …

The explosion in the amount of the available RDF data has lead to the need to explore,
query and understand such data sources. Due to the complex structure of RDF graphs and …

Abstract The Resource Description Framework (RDF) pioneered by the W3C is increasingly
being adopted to model data in a variety of scenarios, in particular data to be published or …

RDF Database Systems is a cutting-edge guide that distills everything you need to know to
effectively use or design an RDF database. This book starts with the basics of linked open …

Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …

Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …

The development of Semantic Web (RDF) brings new requirements for data analytics tools
and methods, going beyond querying to semantics-rich analytics through warehouse-style …

Enterprise RDF knowledge graphs are often built using extraction data pipelines that are fed
by several heterogeneous sources (relational databases, CSV files or even unstructured …

Digital data plays a central role in sciences, journalism, environment, digital humanities, etc.
Open Data sharing initiatives lead to many large, interesting datasets being shared online …

Data trees, typically encoded in JSON, are ubiquitous in data-driven applications. This
ubiquity makes urgent the development of novel techniques for querying heterogeneous …