# Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction

arXiv: https://arxiv.org/abs/2408.08226 · HTML: https://mlsanity.com/paper/2408.08226/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 59 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 24 of 24)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Predictive Multiplicity in Classification](https://mlsanity.com/paper/1909.06677/) (2019)
- [BoxE: A Box Embedding Model for Knowledge Base Completion](https://mlsanity.com/paper/2007.06267/) (2020)
- [Complex Query Answering with Neural Link Predictors](https://mlsanity.com/paper/2011.03459/) (2020)
- [One-Shot Relational Learning for Knowledge Graphs](https://mlsanity.com/paper/1808.09040/) (2018)
- [On Counterfactual Explanations under Predictive Multiplicity](https://mlsanity.com/paper/2006.13132/) (2020)
- [Probabilistic Logic Neural Networks for Reasoning](https://mlsanity.com/paper/1906.08495/) (2019)
- [Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)](https://mlsanity.com/paper/2012.05738/) (2020)
- [Probability Calibration for Knowledge Graph Embedding Models](https://mlsanity.com/paper/1912.10000/) (2019)
- [On Multi-Relational Link Prediction with Bilinear Models](https://mlsanity.com/paper/1709.04808/) (2017)
- [Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods](https://mlsanity.com/paper/2111.03120/) (2021)
- [A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2203.07544/) (2022)
- [Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)](https://mlsanity.com/paper/2312.15097/) (2023)
- [Poisoning Knowledge Graph Embeddings via Relation Inference Patterns](https://mlsanity.com/paper/2111.06345/) (2021)
- [Shrinking Embeddings for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2306.02199/) (2023)
- [Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2307.10219/) (2023)
- [Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings](https://mlsanity.com/paper/2104.05003/) (2021)
- [Generating SROI^- Ontologies via Knowledge Graph Query Embedding Learning](https://mlsanity.com/paper/2407.09212/) (2024)
- [Robust Knowledge Extraction from Large Language Models using Social Choice Theory](https://mlsanity.com/paper/2312.14877/) (2023)

## Built on by (most cited first, 9 of 9)

- [Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees](https://mlsanity.com/paper/2510.24754/) (2025)
- [Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings](https://mlsanity.com/paper/2505.16877/) (2025)
- [AWM: Answerable Working Memory for Long-Document VQA Agents](https://mlsanity.com/paper/2608.25618/) (2026)
- [Resilience in Knowledge Graph Embeddings](https://mlsanity.com/paper/2410.21163/) (2024)
- [Argumentative Ensembling for Robust Recourse under Model Multiplicity](https://mlsanity.com/paper/2506.20260/) (2025)
- [Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings](https://mlsanity.com/paper/2606.03365/) (2026)
- [Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks](https://mlsanity.com/paper/2605.16085/) (2026)
- [SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs](https://mlsanity.com/paper/2607.22571/) (2026)
- [Oracle, will I ever learn? A study of prediction convergence and complementarity across link prediction models](https://mlsanity.com/paper/2609.02638/) (2026)

## Abstract (arXiv)

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 textitpredictive 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.