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PDKE: an efficient distributed embedding framework for large knowledge graphs

conference contribution
posted on 2020-09-01, 00:00 authored by S Dong, X Wang, L Chai, Jianxin LiJianxin Li, Y Yang
Knowledge Representation Learning (KRL) methods produce unsupervised node features from knowledge graphs that can be used for a variety of machine learning tasks. However, two main issues in KRL embedding techniques have not been addressed yet. One is that real-world knowledge graphs contain millions of nodes and billions of edges, which exceeds the capability of existing KRL embedding systems; the other issue is the lack of a unified framework to integrate the current KRL models to facilitate the realization of embeddings for various applications. To address the issues, we propose PDKE, which is a distributed KRL training framework that can incorporate different translation-based KRL models using a unified algorithm template. In PDKE, a set of functions is implemented by various knowledge embedding models to form a unified algorithm template for distributed KRL. PDKE implements training arbitrarily large embeddings in a distributed environment. The effeciency and scalability of our framework have been verified by extensive experiments on both synthetic and real-world knowledge graphs, which shows that our approach outperforms the existing ones by a large margin.

History

Volume

LNCS Vol 12113

Pagination

588-603

Location

Jeju, South Korea

Start date

2020-09-24

End date

2020-09-27

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030594152

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2020, Springer Nature Switzerland

Editor/Contributor(s)

Nah Y, Cui B, Lee S-W, Yu JX, Moon Y-S, Whang SE

Title of proceedings

DASFAA 2020 : Proceedings of the 25th International Conference on Database Systems for Advanced Applications

Event

DASFAA Database Systems for Advanced Applications. International Conference (25th : 2020 : Jeju, South Korea)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

Lecture Notes in Computer Science