Exploration of TransE in a Distributed Environment

Meiyan Lu, L. Liao, Feng Zhang, Dandan Song
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Abstract

Knowledge graph is popular in knowledge mining fields. TransE uses the structure information of triples $\left( {\overrightarrow {{e_h}} + \overrightarrow {{e_r}} \approx \overrightarrow {{e_t}} } \right)$ to embed knowledge graphs into a continuous vector space, which is a very important component in knowledge representations. However, current TransE models are only implemented on single-node machines. With the explosive growth of data volumes, single-node TransE cannot meet the demand for data processing of large knowledge graphs, so a distributed TransE is urgently needed. In this poster, we propose a distributed TransE written in MPI, which can run on HPC clusters. In our experiments, our distributed TransE exhibits high-performance speedup and accuracy.
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分布式环境中transse的探索
知识图谱是知识挖掘领域的热点。TransE利用三元组$\left( {\overrightarrow {{e_h}} + \overrightarrow {{e_r}} \approx \overrightarrow {{e_t}} } \right)$的结构信息将知识图嵌入到连续的向量空间中,这是知识表示中非常重要的组成部分。然而,当前的TransE模型仅在单节点机器上实现。随着数据量的爆炸式增长,单节点TransE已不能满足大型知识图谱的数据处理需求,因此迫切需要分布式TransE。在这张海报中,我们提出了一个用MPI编写的分布式TransE,它可以运行在HPC集群上。在我们的实验中,我们的分布式TransE表现出高性能的加速和准确性。
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