Knowledge graph embedding: a survey from the perspective of representation spaces

Cao, J., Fang, J., Meng, Z. and Liang, S. (2024) Knowledge graph embedding: a survey from the perspective of representation spaces. ACM Computing Surveys, (doi: 10.1145/3643806) (Early Online Publication)

Abstract

Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning and knowledge completion. In this paper, we provide a systematic review of existing KGE techniques based on representation spaces. Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) Algebraic perspective, (2) Geometric perspective, and (3) Analytical perspective. We introduce the rigorous definitions of fundamental mathematical spaces before diving into KGE models and their mathematical properties. We further discuss different KGE methods over the three categories, as well as summarise how spatial advantages work over different embedding needs. By collating the experimental results from downstream tasks, we also explore the advantages of mathematical space in different scenarios and the reasons behind them. We further state some promising research directions from a representation space perspective, with which we hope to inspire researchers to design their KGE models as well as their related applications with more consideration of their mathematical space properties.

• Item Type: Articles
• Keywords: Knowledge graphs, representation spaces, embedding techniques, mathematical perspectives.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Meng, Dr Zaiqiao
• Authors: Cao, J., Fang, J., Meng, Z., and Liang, S.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: ACM Computing Surveys
• Publisher: ACM Press
• ISSN: 0360-0300
• ISSN (Online): 1557-7341
• Published Online: 02 February 2024
• Copyright Holders: Copyright © 2024 held by the owner/author(s)
• First Published: First published in ACM Computing Surveys 2024
• Publisher Policy: Reproduced in accordance with the publisher copyright policy