# Efficient Parallel Translating Embedding For Knowledge Graphs

## TL;DR

This paper introduces ParTrans-X, a parallel framework that significantly accelerates translating embedding methods for knowledge graphs by leveraging graph structures, enabling practical large-scale applications.

## Contribution

The paper presents a novel lock-free parallel framework for translating embedding methods, improving training speed without sacrificing accuracy.

## Key findings

- Speeds up training by over ten times
- Validates with TransE, TransH, and TransE-AdaGrad on two datasets
- Enables practical large-scale knowledge graph embedding

## Abstract

Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [17], and a more efficient variant TransE- AdaGrad [10] validate that ParTrans-X can speed up the training process by more than an order of magnitude.