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

arXiv: https://arxiv.org/abs/2312.15626 · HTML: https://mlsanity.com/paper/2312.15626/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 25 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [Message Passing for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2009.10847/) (2020)
- [On the representation and embedding of knowledge bases beyond binary relations](https://mlsanity.com/paper/1604.08642/) (2016)
- [RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings](https://mlsanity.com/paper/2009.07659/) (2020)
- [Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs](https://mlsanity.com/paper/2009.04404/) (2020)
- [Putting RDF2vec in Order](https://mlsanity.com/paper/2108.05280/) (2021)
- [Walk this Way! Entity Walks and Property Walks for RDF2vec](https://mlsanity.com/paper/2204.02777/) (2022)

## Built on by (most cited first, 3 of 3)

- [Comparison of Metadata Representation Models for Knowledge Graph Embeddings](https://mlsanity.com/paper/2503.21804/) (2025)
- [ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities](https://mlsanity.com/paper/2504.01122/) (2025)
- [gpuRDF2vec -- Scalable GPU-based RDF2vec](https://mlsanity.com/paper/2508.01073/) (2025)

## Abstract (arXiv)

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.