Zhu, Yuqicheng, Potyka, Nico, Nayyeri, Mojtaba, Xiong, Bo, He, Yunjie, Kharlamov, Evgeny and Staab, Steffen
2024.
Predictive multiplicity of knowledge graph embeddings in link prediction.
Presented at: The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024),
Miami, Florida,
12-16 November 2024.
Published in: Al-Onaizan, Yaser, Bansal, Mohit and Chen, Yun-Nung eds.
Findings of the Association for Computational Linguistics: EMNLP 2024.
Association for Computational Linguistics,
pp. 334-354.
10.18653/v1/2024.findings-emnlp.19
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Abstract
Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link prediction yet give conflicting predictions for unseen queries. This phenomenon is termed predictive multiplicity in the literature. It poses substantial risks for KGE-based applications in high-stake domains but has been overlooked in KGE research. We define predictive multiplicity in link prediction, introduce evaluation metrics and measure predictive multiplicity for representative KGE methods on commonly used benchmark datasets. Our empirical study reveals significant predictive multiplicity in link prediction, with 8% to 39% testing queries exhibiting conflicting predictions. We address this issue by leveraging voting methods from social choice theory, significantly mitigating conflicts by 66% to 78% in our experiments.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Publisher: | Association for Computational Linguistics |
| ISBN: | 9798891761681 |
| Date of First Compliant Deposit: | 14 October 2024 |
| Date of Acceptance: | 20 September 2024 |
| Last Modified: | 18 Mar 2026 11:29 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/172865 |
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