  Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data
represent relationships between various entities through the structure of
triples (<subject, predicate, object>). Knowledge graph embedding (KGE) is
crucial in machine learning applications, specifically in node classification
and link prediction tasks. KGE remains a vital research topic within the
semantic web community. RDF-star introduces the concept of a quoted triple
(QT), a specific form of triple employed either as the subject or object within
another triple. Moreover, RDF-star permits a QT to act as compositional
entities within another QT, thereby enabling the representation of recursive,
hyper-relational KGs with nested structures. However, existing KGE models fail
to adequately learn the semantics of QTs and entities, primarily because they
do not account for RDF-star graphs containing multi-leveled nested QTs and
QT-QT relationships. This study introduces RDF-star2Vec, a novel KGE model
specifically designed for RDF-star graphs. RDF-star2Vec introduces graph walk
techniques that enable probabilistic transitions between a QT and its
compositional entities. Feature vectors for QTs, entities, and relations are
derived from generated sequences through the structured skip-gram model.
Additionally, we provide a dataset and a benchmarking framework for data mining
tasks focused on complex RDF-star graphs. Evaluative experiments demonstrated
that RDF-star2Vec yielded superior performance compared to recent extensions of
RDF2Vec in various tasks including classification, clustering, entity
relatedness, and QT similarity.
