# Probability Calibration for Knowledge Graph Embedding Models

arXiv: https://arxiv.org/abs/1912.10000 · HTML: https://mlsanity.com/paper/1912.10000/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 21 of 21)

- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [How to Turn Your Knowledge Graph Embeddings into Generative Models](https://mlsanity.com/paper/2305.15944/) (2023)
- [Adapting Neural Link Predictors for Data-Efficient Complex Query Answering](https://mlsanity.com/paper/2301.12313/) (2023)
- [Membership Inference Attacks on Knowledge Graphs](https://mlsanity.com/paper/2104.08273/) (2021)
- [Bilinear Scoring Function Search for Knowledge Graph Learning](https://mlsanity.com/paper/2107.00184/) (2021)
- [Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction](https://mlsanity.com/paper/2408.08226/) (2024)
- [Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees](https://mlsanity.com/paper/2510.24754/) (2025)
- [Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings](https://mlsanity.com/paper/2201.00565/) (2022)
- [Stay Positive: Knowledge Graph Embedding Without Negative Sampling](https://mlsanity.com/paper/2201.02661/) (2022)
- [Conformalized Answer Set Prediction for Knowledge Graph Embedding](https://mlsanity.com/paper/2408.08248/) (2024)
- [Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings](https://mlsanity.com/paper/2505.16877/) (2025)
- [Kronecker Decomposition for Knowledge Graph Embeddings](https://mlsanity.com/paper/2205.06560/) (2022)
- [Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples](https://mlsanity.com/paper/2212.02651/) (2022)
- [Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?](https://mlsanity.com/paper/2301.12929/) (2023)
- [Perception and Semantic Aware Regularization for Sequential Confidence Calibration](https://mlsanity.com/paper/2305.19498/) (2023)
- [Generalized Reciprocal Perspective](https://mlsanity.com/paper/2210.11616/) (2022)
- [Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction](https://mlsanity.com/paper/2306.00041/) (2023)
- [PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing](https://mlsanity.com/paper/2308.08371/) (2023)
- [Lightweight Inference-Time Personalization for Frozen Knowledge Graph Embeddings](https://mlsanity.com/paper/2512.22398/) (2025)
- [A hierarchy of faithfulness criteria for knowledge base completion](https://mlsanity.com/paper/2609.27863/) (2026)
- [Uncertainty in Representation Learning on Knowledge Graphs](https://mlsanity.com/paper/2610.06974/) (2026)

## Abstract (arXiv)

Knowledge graph embedding research has overlooked the problem of probability calibration. We show popular embedding models are indeed uncalibrated. That means probability estimates associated to predicted triples are unreliable. We present a novel method to calibrate a model when ground truth negatives are not available, which is the usual case in knowledge graphs. We propose to use Platt scaling and isotonic regression alongside our method. Experiments on three datasets with ground truth negatives show our contribution leads to well-calibrated models when compared to the gold standard of using negatives. We get significantly better results than the uncalibrated models from all calibration methods. We show isotonic regression offers the best the performance overall, not without trade-offs. We also show that calibrated models reach state-of-the-art accuracy without the need to define relation-specific decision thresholds.