ParaGraphE: A Library for Parallel Knowledge Graph Embedding
This work addresses efficiency issues for researchers and practitioners working with large-scale knowledge graphs, but it is incremental as it focuses on parallelizing existing methods rather than introducing new embedding techniques.
The authors tackled the problem of time-consuming single-thread implementations for knowledge graph embedding methods by designing ParaGraphE, a unified parallel framework that significantly reduces computation time without affecting accuracy.
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 .