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arXiv · 2312.15626

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

Shusaku Egami·Takanori Ugai·Masateru Oota·Kyoumoto Matsushita·Takahiro Kawamura·Kouji Kozaki·Ken Fukuda
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Abstract

Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples ( ). 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.

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BibTeXRIS

Shusaku Egami, Takanori Ugai, Masateru Oota, Kyoumoto Matsushita, Takahiro Kawamura, Kouji Kozaki, Ken Fukuda. 2023-12-25. RDF-star2Vec: RDF-star Graph Embeddings for Data Mining. https://doi.org/10.1109/access.2023.3341029

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