  Knowledge graph embedding aims at translating the knowledge graph into
numerical representations by transforming the entities and relations into
continuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have
been proposed to deal with this problem, but existing single-thread
implementations 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 influencing 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 .
