用GPU加速知识图谱嵌入，训练速度远超现有方法。
gpuRDF2vec -- Scalable GPU-based RDF2vec
- 基于GPU和多节点并行，加速RDF2vec全流程
- 单机环境下图遍历阶段比现有工具快数倍
- 适合需要快速训练大规模知识图谱嵌入的研究者
在网页规模下生成知识图谱嵌入仍具挑战性。现有方法中，RDF2vec兼具效果与可扩展性。本文提出gpuRDF2vec，一个开源库，利用现代GPU并支持多节点执行，加速RDF2vec管道的每一阶段。在合成图和真实世界基准上的大量实验表明，gpuRDF2vec相比当前最快方案jRDF2vec实现了显著加速。单机设置下，其随机游走提取阶段在大规模/密集图上显著优于pyRDF2vec、SparkKGML和jRDF2vec，且能良好扩展至更长游走路径，通常带来更高质量嵌入。gpuRDF2vec基于Pytorch Lightning实现可扩展word2vec，使研究人员和实践者能在合理时间内训练大规模知识图谱高质量嵌入。
原文摘要 · Abstract (English)
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.
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