Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2505.16877v1
• Date: Thu, 22 May 2025 16:33:20 GMT
• Title: Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings
• Authors: Yuqicheng Zhu, Daniel Hernández, Yuan He, Zifeng Ding, Bo Xiong, Evgeny Kharlamov, Steffen Staab,
• Abstract summary: CondKGCP approximates predicate-conditional coverage guarantees while maintaining compact prediction sets.<n>We prove the theoretical guarantees and demonstrate empirical effectiveness of CondKGCP by comprehensive evaluations.
• Score: 27.138935426612306
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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