Conformalized Answer Set Prediction for Knowledge Graph Embedding
This addresses uncertainty quantification for knowledge graph embeddings, enabling safer use in high-stakes domains like medicine, though it is incremental as it applies existing conformal prediction theory to KGE.
The paper tackled the problem of knowledge graph embeddings lacking probabilistic interpretation for uncertainty quantification by applying conformal prediction to generate answer sets with guaranteed coverage, validating on four benchmarks with six KGE methods that the sets meet theoretical guarantees and adapt in size to query difficulty.
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.