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arXiv · 1703.05614

ParaGraphE: A Library for Parallel Knowledge Graph Embedding

Xiao-Fan Niu·Wu-Jun Li
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Abstract

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 .

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Xiao-Fan Niu, Wu-Jun Li. 2017-03-16. ParaGraphE: A Library for Parallel Knowledge Graph Embedding. https://arxiv.org/abs/1703.05614

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