Conformalized Answer Set Prediction for Knowledge Graph Embedding

TL;DR

This paper introduces a conformal prediction framework for knowledge graph embeddings, enabling probabilistic guarantees for answer sets, which improves uncertainty quantification in high-stakes applications like medicine.

Contribution

It applies conformal prediction to knowledge graph embeddings, providing a method to generate answer sets with probabilistic guarantees and adaptive sizes.

Findings

Answer sets satisfy probabilistic guarantees

Generated answer sets have sensible and adaptive sizes

Method validated on four benchmark datasets

Abstract

Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies. The learned KG embeddings are typically used to answer queries by ranking all potential answers, but rankings often lack a meaningful probabilistic interpretation - lower-ranked answers do not necessarily have a lower probability of being true. This limitation makes it difficult to quantify uncertainty of model's predictions, posing challenges for the application of KGE methods in high-stakes domains like medicine. We address this issue by applying the theory of conformal prediction that allows generating answer sets, which contain the correct answer with probabilistic guarantees. We explain how conformal prediction can be used to generate such answer sets for link prediction tasks. Our empirical evaluation on four benchmark datasets using six representative KGE methods validates that the generated answer sets satisfy the probabilistic guarantees given by the theory of conformal prediction. We also demonstrate that the generated answer sets often have a sensible size and that the size adapts well with respect to the difficulty of the query.