ParaGraphE: A Library for Parallel Knowledge Graph Embedding

TL;DR

ParaGraphE is a parallel framework that accelerates knowledge graph embedding methods significantly without sacrificing accuracy, enabling efficient processing of large-scale graphs.

Contribution

It introduces a unified parallel framework for knowledge graph embedding methods, reducing computation time while maintaining accuracy.

Findings

Achieves significant time reduction in embedding large-scale knowledge graphs

Maintains embedding accuracy comparable to single-thread implementations

Provides an open-source library for parallel knowledge graph embedding

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 .