RDF-star2Vec: RDF-star Graph Embeddings for Data Mining
Shusaku Egami Takanori Ugai Masateru Oota Kyoumoto Matsushita Takahiro Kawamura Kouji Kozaki Ken Fukuda
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.