  In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF
data into data representations tailored for graph machine learning tasks.
AutoRDF2GML enables, for the first time, the creation of both content-based
features -- i.e., features based on RDF datatype properties -- and
topology-based features -- i.e., features based on RDF object properties.
Characterized by automated feature extraction, AutoRDF2GML makes it possible
even for users less familiar with RDF and SPARQL to generate data
representations ready for graph machine learning tasks, such as link
prediction, node classification, and graph classification. Furthermore, we
present four new benchmark datasets for graph machine learning, created from
large RDF knowledge graphs using our framework. These datasets serve as
valuable resources for evaluating graph machine learning approaches, such as
graph neural networks. Overall, our framework effectively bridges the gap
between the Graph Machine Learning and Semantic Web communities, paving the way
for RDF-based machine learning applications.
