Spectral Initialization and Scheduled Graph Smoothness for Uncertain Knowledge Graph Completion

2026-09-02

AI Summary

为解决不确定知识图谱三元组信心预测问题，QUEST方法通过谱初始化和图平滑调度结合狄利克雷能量正则化提高预测准确性和训练稳定性。

Abstract

Uncertain knowledge graphs (UKGs) extend knowledge graphs by assigning each triple a continuous confidence score. Since most possible triples lack observed confidences, recent methods rely on semi-supervised learning to generate pseudo-labels. These methods initialize entity embeddings without using the confidence-weighted graph, discarding its global community and hub structure. We introduce QUEST, which adds no trainable parameters to the standard confidence-distribution learning pipeline. First, QUEST initializes entity embeddings using the smallest non-trivial eigenvectors of the confidence-weighted graph Laplacian, incorporating community and hub structure before training. Second, QUEST applies an unbiased mini-batch Dirichlet energy regularizer to enforce early-stage structural consistency. On two UKG datasets, QUEST improves confidence prediction and link prediction on six of eight metric-dataset pairs over prior methods and matches the previous best on the remaining two, while removing the instability spike observed on dense graphs. These results indicate that spectral structural priors combined with a graph Dirichlet energy regularizer improve accuracy, training stability, and checkpoint reliability in UKG completion.

Problem

• Uncertain Knowledge Graphs
• Confidence Scores
• Semi-supervised Learning
• Entity Embeddings
• Graph Laplacian

Innovation

• Spectral Initialization
• Graph Dirichlet Energy Regularizer
• Confidence-Weighted Graph Laplacian