Knowledge graph embedding aims at translating the knowledge graph into numerical representations by transforming the entities and relations into con- tinuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have been proposed to deal with this problem, but existing single-thread implemen- tations of them are time-consuming for large-scale knowledge graphs. Here, we design a unified parallel framework to parallelize these methods, which achieves a significant time reduction without in uencing the accuracy. We name our framework as ParaGraphE, which provides a library for parallel knowledge graph embedding. The source code can be downloaded from https: //github.com/LIBBLE/LIBBLE-MultiThread/tree/master/ParaGraphE.
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