| Autor: |
Khatbane, Mohammed, Islam, Md Kamrul, Aridhi, Sabeur, Smaíl-Tabbone, Malika |
| Předmět: |
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| Zdroj: |
Procedia Computer Science; 2024, Vol. 246, p900-909, 10p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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