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 …

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 …

The “RNA world” represents a novel frontier for the study of fundamental biological
processes and human diseases and is paving the way for the development of new drugs …

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 …

RDF graphs are commonly derived from (semi-) structured data by applying a set of
map**s. RDF graph construction is performed by either materialising or virtualising an …

Abstract Knowledge Graphs are currently created using an assortment of techniques and
tools: ad hoc code in a programming language, database export scripts, OpenRefine …

RDF graphs are often generated by map** data in other (semi-) structured data formats to
RDF. Such mapped graphs have a repetitive structure defined by (i) the map** rules and …

Knowledge graphs have proven to be a powerful technology to integrate and structure the
myriad of data available nowadays. The semantic web community has actively worked on …