Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings (2505.16877v1)

Abstract: Uncertainty quantification in Knowledge Graph Embedding (KGE) methods is crucial for ensuring the reliability of downstream applications. A recent work applies conformal prediction to KGE methods, providing uncertainty estimates by generating a set of answers that is guaranteed to include the true answer with a predefined confidence level. However, existing methods provide probabilistic guarantees averaged over a reference set of queries and answers (marginal coverage guarantee). In high-stakes applications such as medical diagnosis, a stronger guarantee is often required: the predicted sets must provide consistent coverage per query (conditional coverage guarantee). We propose CondKGCP, a novel method that approximates predicate-conditional coverage guarantees while maintaining compact prediction sets. CondKGCP merges predicates with similar vector representations and augments calibration with rank information. We prove the theoretical guarantees and demonstrate empirical effectiveness of CondKGCP by comprehensive evaluations.

Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings

The paper "Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings" introduces a novel methodology called CondKGCP, aimed at enhancing uncertainty quantification in Knowledge Graph Embedding (KGE) models. The focus of this research is on predicate-conditional coverage guarantees, a crucial enhancement over existing marginal coverage methods, particularly useful in high-stakes applications like medical diagnosis.

Methodological Advancements

Conformal prediction frameworks are leveraged effectively, providing a systematic approach to uncertainty quantification by generating answer sets that meet a predefined confidence level. The standard conformal methods deliver probabilistic assurances on average, across a reference set, which may not suffice for scenarios demanding query-specific reliability. This research addresses this limitation by introducing predicate-conditional coverage guarantees, ensuring consistent coverage per query.

CondKGCP innovatively merges predicates with similar vector representations, bolstering the number of available calibration triples, essential for robust subgroup-level conformal prediction. This strategic merging reduces the variance in predictive uncertainty across different predicates by assuming similarity in nonconformity score distributions. Complementing this, CondKGCP implements a dual calibration schema that integrates score calibration with rank-based filtering. This dual approach efficiently reduces the prediction set size by excluding entities with noisy or unreliable scores.

Theoretical and Empirical Results

The paper provides theoretical assurances of conditional coverage probability, indicating proximity to the desired confidence levels, with defined bounds. The incorporation of rank calibration demonstrates potential in significant reduction of prediction set sizes, contingent on certain data conditions.

Empirically, CondKGCP is evaluated against five baseline methods across multiple datasets, confirming its superiority in balancing conditional coverage probability with prediction set size. This method consistently achieves optimal performance, marked by efficient use of calibration triples and reliable conditional coverage guarantees.

Practical and Theoretical Implications

The implications of this work are extensive in the field of AI, especially for high-stakes applications requiring reliable uncertainty quantification. CondKGCP can be adapted easily for conditional guarantees beyond predicates, such as entity-type or other subgroup characteristics, with potential extension into tasks like query answering and probabilistic inference in KGE-based reasoning systems. This work contributes to the broader challenge of ensuring trustworthy AI systems, particularly in fields where erroneous predictions carry significant risks.

Future Directions in AI Development

Future research could explore enhancing the predicate merging process by incorporating semantic features, augmenting robustness across diverse real-world scenarios. Additionally, extending this framework to accommodate covariate shift will enhance its applicability in dynamic, real-world environments where data distributions evolve.

This paper stands as a significant contribution to the discourse on uncertainty management in AI systems, paving the way for more nuanced and dependable modeling methods essential for critical applications.