gpuRDF2vec-Scalable GPU-Based RDF2vec

Boeckling, Martin

doi:10.1007/978-3-032-09530-5_14

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gpuRDF2vec-Scalable GPU-Based RDF2vec

2026-01-01

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Boeckling, Martin *

Paulhe, Heiko

DOI：10.1007/978-3-032-09530-5_14

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Original request for help

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Abstract

En 中文

Generating Knowledge Graph (KG) embeddings at web scale remains challenging. Among existing techniques, RDF2vec combines effectiveness with strong scalability. We present gpuRDF2vec, an open source library that harnesses modern GPUs and supports multi-node execution to accelerate every stage of the RDF2vec pipeline. Extensive experiments on both synthetically generated graphs and real-world benchmarks show that gpuRDF2vec achieves up to a substantial speedup over the currently fastest alternative, i.e., jRDF2vec. In a single-node setup, our walk-extraction phase alone outperforms pyRDF2vec, SparkKGML, and jRDF2vec by a substantial margin using random walks on large/ dense graphs, and scales very well to longer walks, which typically lead to better quality embeddings. Our implementation of gpuRDF2vec enables practitioners and researchers to train high-quality KG embeddings on large-scale graphs within practical time budgets and builds on top of Pytorch Lightning for the scalable word2vec implementation (1)(Our github repository can be found under the following link and can be downloaded as a pypi package).

Keywords:

RDF2vec

Distributed Computing

GPU processing

Journal

SEMANTIC WEB-ISWC 2025, PT II

IF:

Papers:

Citations:

Organization

university of mannheim

Scholars:

269

Papers: 180

Citations: 0

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