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Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees

graph-neural-networks › knowledge-graphs

📄 Abstract

Abstract: Uncertain knowledge graph embedding (UnKGE) methods learn vector representations that capture both structural and uncertainty information to predict scores of unseen triples. However, existing methods produce only point estimates, without quantifying predictive uncertainty-limiting their reliability in high-stakes applications where understanding confidence in predictions is crucial. To address this limitation, we propose \textsc{UnKGCP}, a framework that generates prediction intervals guaranteed to contain the true score with a user-specified level of confidence. The length of the intervals reflects the model's predictive uncertainty. \textsc{UnKGCP} builds on the conformal prediction framework but introduces a novel nonconformity measure tailored to UnKGE methods and an efficient procedure for interval construction. We provide theoretical guarantees for the intervals and empirically verify these guarantees. Extensive experiments on standard benchmarks across diverse UnKGE methods further demonstrate that the intervals are sharp and effectively capture predictive uncertainty.

Authors (7)

Yuqicheng Zhu

Jingcheng Wu

Yizhen Wang

Hongkuan Zhou

Jiaoyan Chen

Evgeny Kharlamov

+1 more

Submitted

October 18, 2025

arXiv Category

stat.ML

arXiv PDF

Key Contributions

This paper introduces \textsc{UnKGCP}, a framework that extends Uncertain Knowledge Graph Embedding (UnKGE) methods to provide prediction intervals with statistical guarantees. By leveraging conformal prediction and a novel nonconformity measure, \textsc{UnKGCP} quantifies predictive uncertainty, offering reliable confidence estimates crucial for high-stakes applications.

Business Value

Increases the trustworthiness and reliability of knowledge graph-based systems, enabling better decision-making in domains like finance, healthcare, and risk assessment where uncertainty is critical.

Paper Metadata

Innovation Type

Framework for Uncertainty Quantification

Deployment Feasibility

Feasible for integration into existing knowledge graph embedding pipelines, requiring implementation of the UnKGCP framework.

Limitations Addressed

Existing UnKGE methods produce only point estimates without quantifying predictive uncertainty, limiting their reliability in critical applications.

Performance Gains

Provides prediction intervals guaranteed to contain the true score with user-specified confidence, unlike point estimates from prior methods.

Technical Tags

Uncertain Knowledge Graph Embedding (UnKGE)conformal predictionprediction intervalsuncertainty quantificationnonconformity measuretheoretical guaranteesknowledge graph embeddingtriple predictionstatistical guarantees

Research Topics

Knowledge RepresentationMachine LearningUncertainty QuantificationGraph EmbeddingsData Mining

Methods & Architectures

UnKGCP frameworkConformal predictionNovel nonconformity measureEfficient interval construction UnKGCP (framework)Models for UnKGE

Applications & Tasks

Knowledge Graph Completion Data Mining Information Retrieval Decision Support Systems Quantifying predictive uncertainty in UnKGEGenerating reliable prediction intervalsImproving reliability in high-stakes applications Learn vector representations for uncertain knowledge graphsGenerate prediction intervals with user-specified confidenceQuantify predictive uncertainty

Datasets & Benchmarks

Datasets

Standard benchmarks

Prediction interval coverageInterval lengthEmpirical verification of theoretical guarantees

Related Fields

Knowledge GraphsMachine LearningUncertainty QuantificationData MiningStatistics

Keywords

uncertain knowledge graphsknowledge graph embeddinguncertainty quantificationconformal predictionprediction intervalstriple predictionstatistical guaranteesUnKGEUnKGCPmachine learningdata mining

Academic Context

#Knowledge Representation#Machine Learning#Uncertainty Quantification#Graph Embeddings#Data Mining

Technology Stack

Frameworks & Libraries

UnKGCP

Commercial Potential

Potential Products

Reliable knowledge graph completion systemsDecision support tools with uncertainty estimatesRisk assessment platforms

Target Industries

FinanceHealthcareE-commerceInformation TechnologyResearch

Use Case Examples

Predicting relationships in financial knowledge graphs with confidence boundsAssessing the reliability of drug interaction predictionsProviding uncertainty estimates for recommendations in e-commerce

Competitive Edge

Offers a significant advantage over existing UnKGE methods by providing statistically guaranteed prediction intervals, enabling more reliable downstream applications.

Market Opportunity

Growing market for knowledge graph technologies and AI solutions requiring robust uncertainty estimation.

Revenue Models

Licensing of the UnKGCP framework or integration into AI platforms.

Resource Requirements

Compute Needs

Requires computational resources for training UnKGE models and applying the conformal prediction framework.

Data Requirements

Requires standard benchmarks for UnKGE methods.

Deployment Constraints

Integration requires compatibility with existing knowledge graph databases and embedding models.

Scalability

The efficiency of interval construction is highlighted, suggesting good scalability.

Regulatory Considerations

High potential impact on regulated industries (financehealthcare) where uncertainty quantification is crucial.

Production Readiness

Maturity Level

Research

Time to Market

2-4 years for integration into enterprise knowledge graph solutions.

Patent Potential

Moderate, related to the novel nonconformity measure and interval construction procedure.

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