More and more data in various formats are integrated into knowledge graphs. However,
there is no overview of existing approaches for generating knowledge graphs from …

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

Data integration is the dominant use case for RDF Knowledge Graphs. However, Web
resources come in formats with weak semantics (for example, CSV and JSON), or formats …

Abstract The Relational to RDF Map** Language (R2RML) became a W3C
Recommendation a decade ago. Despite its wide adoption, its potential applicability beyond …

Abstract Knowledge graphs are often constructed from heterogeneous data sources, using
declarative rules that map them to a target ontology and materializing them into RDF. When …

With knowledge graphs (KGs) at the center of numerous applications such as recommender
systems and question answering, the need for generalized pipelines to construct and …

In recent years, data lakes emerged as a way to manage large amounts of heterogeneous
data for modern data analytics. One way to prevent data lakes from turning into inoperable …

In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the
power of integrating disparate sources of knowledge to discover adverse drug effects …

RDF knowledge graphs (KG) are powerful data structures to represent factual statements
created from heterogeneous data sources. KG creation is laborious and demands data …

The hard drive is one of the important components of a computing system, and its failure can
lead to both system failure and data loss. Therefore, the reliability of a hard drive is very …