论文标题
时间知识图完成：调查
Temporal Knowledge Graph Completion: A Survey
论文作者
论文摘要
知识图完成（KGC）可以预测缺失的链接，并且对于现实世界知识图至关重要，而现实世界中的知识图很普遍。 KGC方法假设知识图是静态的，但是这可能导致预测结果不准确，因为知识图中的许多事实随时间变化。最近，新兴方法通过进一步纳入事实的时间戳，显示出改善的预测结果。即，时间知识图完成（TKGC）。借助此时间信息，TKGC方法可以学习KGC方法无法捕获的知识图的动态演变。在本文中，我们首次总结了TKGC研究的最新进展。首先，我们详细介绍了TKGC的背景，包括问题定义，基准数据集和评估指标。然后，我们根据如何使用事实的时间戳来捕获时间动力学来总结现有的TKGC方法。最后，我们总结了本文，并提出了TKGC的未来研究指示。
Knowledge graph completion (KGC) can predict missing links and is crucial for real-world knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Recently, emerging methods have shown improved predictive results by further incorporating the timestamps of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods can learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the problem definition, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how timestamps of facts are used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC.
