论文标题
学习时间知识图图图链接预测的一击关系的元表示
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
论文作者
论文摘要
近年来，很少有静态知识图（kg）的关系学习图（kgs）引起了更大的兴趣，而几乎没有研究了时间知识图（TKG）的少数学习。与公斤相比，TKG包含丰富的时间信息，因此需要建模的时间推理技术。在时间上，学习很少的射击关系会带来更大的挑战。在本文中，我们遵循以前的工作，该工作着重于静态KG上的几乎没有关系的学习，并扩展了两个基本的TKG推理任务，即插值和推断的链接预测到一杆设置。我们提出了四个新的大型基准数据集，并开发了用于在TKG中学习一次性关系的TKG推理模型。实验结果表明，我们的模型可以在两个TKG链接预测任务中的所有数据集上实现卓越的性能。
Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly been studied. Compared to KGs, TKGs contain rich temporal information, thus requiring temporal reasoning techniques for modeling. This poses a greater challenge in learning few-shot relations in the temporal context. In this paper, we follow the previous work that focuses on few-shot relational learning on static KGs and extend two fundamental TKG reasoning tasks, i.e., interpolated and extrapolated link prediction, to the one-shot setting. We propose four new large-scale benchmark datasets and develop a TKG reasoning model for learning one-shot relations in TKGs. Experimental results show that our model can achieve superior performance on all datasets in both TKG link prediction tasks.
