RDF-star2Vec: RDF-star Graph Embeddings for Data Mining

• Published in: IEEE access Vol. 11; pp. 142030 - 142042
• Main Authors: Egami, Shusaku, Ugai, Takanori, Oota, Masateru, Matsushita, Kyoumoto, Kawamura, Takahiro, Kozaki, Kouji, Fukuda, Ken
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmark testing; Classification; Clustering; Data mining; graph walk; Graphs; hyper-relational knowledge graphs; Knowledge graph embedding; Knowledge graphs; Knowledge representation; Machine learning; N-ary relation; RDF-star; RDF2Vec; Resource description framework; Resource Description Framework-RDF; Semantic web; Semantics; Task analysis; Task complexity
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3341029

Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<inline-formula> <tex-math notation="LaTeX"> < subject </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">predicate </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">object> </tex-math></inline-formula>). 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.