One-shot relational learning for extrapolation reasoning on temporal knowledge graphs

Ruixin Ma, Biao Mei, Yunlong Ma, Hongyan Zhang, Meihong Liu, Liang Zhao

Abstract: In recent years, temporal knowledge graph reasoning has been a critical task in natural language processing. Temporal knowledge graphs store temporal facts that model dynamic relationships or interactions between entities along the timeline. Most existing temporal knowledge graph reasoning methods need a large number of training instances (i.e. support entity facts) for each relation. However, the same as traditional knowledge graphs, temporal knowledge graphs also exhibit long-tailed relational frequency distribution, in which most relationships often do not have many support entity pairs for training. To address this problem, in this paper, we propose a one-shot learning framework (OSLT) applied to temporal knowledge graph link prediction, which aims to predict new relational facts with only one support instance. Specifically, OSLT employs an fact encoder based on Temporal Convolutional Network to encode historical information and model connection of facts at the same timestamp by the aggregator with an attention mechanism. After that, a matching network is employed to compute the similarity score between support fact and query fact. Experiments show that the proposed method outperforms the state-of-the-art baselines on two benchmark datasets.