论文标题
缩放知识图嵌入模型
Scaling Knowledge Graph Embedding Models
论文作者
论文摘要
由于高数据依赖性需要高度计算成本和巨大的内存足迹，为链接预测任务开发可扩展的解决方案用于链接预测任务是具有挑战性的。我们提出了一种新方法，用于扩展知识图嵌入模型的链接预测模型以应对这些挑战。为此，我们提出了以下算法策略：自给自足的分区，基于约束的负抽样和边缘迷你批次培训。分区策略和基于约束的负抽样都可以避免在培训期间进行跨分区数据传输。在我们的实验评估中，我们表明，基于GNN的知识图嵌入模型的缩放解决方案在基准数据集上达到了16倍的速度，同时将可比较的模型性能作为标准指标上的非分配方法。
Developing scalable solutions for training Graph Neural Networks (GNNs) for link prediction tasks is challenging due to the high data dependencies which entail high computational cost and huge memory footprint. We propose a new method for scaling training of knowledge graph embedding models for link prediction to address these challenges. Towards this end, we propose the following algorithmic strategies: self-sufficient partitions, constraint-based negative sampling, and edge mini-batch training. Both, partitioning strategy and constraint-based negative sampling, avoid cross partition data transfer during training. In our experimental evaluation, we show that our scaling solution for GNN-based knowledge graph embedding models achieves a 16x speed up on benchmark datasets while maintaining a comparable model performance as non-distributed methods on standard metrics.
