DKGR: A Distributed Framework for Geometric Knowledge Graph Embedding with Ray

Abstract

In recent years, researchers were interested in learning knowledge graph (KG) embeddings because of their advantages in downstream tasks like link prediction, entity disambiguation, and entity Classification in KGs. The state-of-the-art KG embedding models are mainly implemented in a centralized environment, i.e., they are trained on a single machine. However, most of the real-world KGs are huge in size, and a single machine is not enough to store the whole KG and train embedding models in this context. There are a few frameworks for learning KG embeddings in distributed environments. In this paper, we propose a novel distributed framework, namely DKGR, for training geometric embedding models. Our framework uses the Ray platform as a distributed environment. We evaluate our framework for the link prediction task on real-world datasets. Our preliminary results show a good speed-up when training KG embedding models in our framework. They also show that DKGR is scalable to large-scale KGs without affecting the link prediction performance remarkably.