Charpenay, Victor and Schockaert, Steven  ORCID: https://orcid.org/0000-0002-9256-2881
2025.
Less Is MuRE: Revisiting shallow knowledge graph embeddings.
Presented at: The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP),
Suzhou, China,
4-9 November 2025.
Published in: Christodoulopoulos, Christos, Chakraborty, Tanmoy, Rose, Carolyn and Peng, Violet eds.
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.
Suzhou, China:
Association for Computational Linguistics,
pp. 15417-15443.
10.18653/v1/2025.emnlp-main.779
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Abstract
In recent years, the field of knowledge graph completion has focused on increasingly sophisticated models, which perform well on link prediction tasks, but are less scalable than earlier methods and are not suitable for learning entity embeddings. As a result, shallow models such as TransE and ComplEx remain the most popular choice in many settings. However, the strengths and limitations of such models remain poorly understood. In this paper, we present a unifying framework and systematically analyze a number of variants and extensions of existing shallow models, empirically showing that MuRE and its extension, ExpressivE, are highly competitive. Motivated by the strong empirical results of MuRE, we also theoretically analyze the expressivity of its associated scoring function, surprisingly finding that it can capture the same class of rule bases as state-of-the-art region-based embedding models.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Association for Computational Linguistics |
| ISBN: | 979-8-89176-332-6 |
| Date of First Compliant Deposit: | 21 October 2025 |
| Date of Acceptance: | 20 August 2025 |
| Last Modified: | 30 Jan 2026 16:41 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/181775 |
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