Tackling Long-Tail Entities for Temporal Knowledge Graph Completion

Mehrnoosh Mirtaheri, Ryan A. Rossi, Sungchul Kim, Kanak Mahadik, Tong Yu, Xiang Chen, Mohammad Rostami

Abstract: Most Temporal Knowledge Graphs (TKGs) exhibit a long-tail entity distribution, where the majority of entities have sparse connections. Existing TKG completion methods struggle with managing new or unseen entities that often lack sufficient connections. In this paper, we introduce a model-agnostic enhancement layer that can be integrated with any existing TKG completion method to improve its performance. This enhancement layer employs a broader, global definition of entity similarity, transcending the limitations of local neighborhood proximity found in Graph Neural Network (GNN) based methods. Additionally, we conduct our evaluations in a novel, realistic setup that treats the TKG as a stream of evolving data. Evaluations on two benchmark datasets demonstrate that our framework surpasses existing methods in overall link prediction, inductive link prediction, and in addressing long-tail entities. Notably, our approach achieves a 10% improvement in MRR on one dataset and a 15% increase on another.