Oracle, will I ever learn? A study of prediction convergence and complementarity across link prediction models

2026-09-02

AI Summary

研究通过分析不同链接预测模型的互补性，探讨了它们在知识图谱中的预测收敛性和互补性问题，并提出了一种测量方法来评估模型组合的潜在性能。

Abstract

Knowledge graphs have become an important source of structured knowledge for Web applications, including search, question answering, and recommender systems. In these applications, link prediction can serve either as a prediction task itself or as a means to enrich incomplete knowledge graphs for downstream tasks. Interestingly, different link prediction models, or even different training runs of the same model, can produce substantially different predictions for the same query. This suggests a variability in the capture of the underlying knowledge by models, thus raising a fundamental question: to what extent do different models capture complementary knowledge, and how much of this knowledge could be recovered by combining them? We propose to measure model complementarity through the performance of an oracle that, for each query, selects the best prediction among a considered set of models, hence providing an upper bound on the performance achievable through model combination. Across several architectures and benchmarks, we find a substantial gap between individual models and their oracle, revealing that different models capture complementary knowledge. Yet, this complementarity rapidly saturates as more models are added, leaving a persistent subset of queries unsolved even by a large number of models. These findings reveal both the potential of model complementarity and a fundamental limit to what current link prediction models can collectively recover; thereby highlighting the need for further research to build robust Web applications.

Problem

• link prediction
• knowledge graphs
• model complementarity
• oracle performance

Innovation

• model complementarity
• link prediction
• knowledge graphs
• oracle performance