从历史中学习：时序知识图谱的顺序复制生成网络建模
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
摘要：
大型知识图谱通常会增长以存储时间事实，这些时间事实对实体沿时间轴的动态关系或交互进行建模。由于这样的时间知识图谱经常遭受不完整性，重要的是开发时间感知的表示学习模型，帮助推断缺失的时间事实。虽然时间事实通常是不断演变的，但据观察，许多事实往往显示出沿时间轴的重复模式，如经济危机和外交活动。这一观察表明，模型可以从历史上出现的已知事实中学到很多东西。为此，我们提出了一个新的表示学习模型的时间知识图谱，即CyGNet，基于一个新的时间感知副本生成机制。CyGNet不仅能够从整个实体词汇中预测未来的事实，而且能够识别具有重复性的事实，并参考过去的已知事实来预测这些未来的事实。我们使用五个基准数据集评估所提出的方法上的知识图谱完成任务。大量的实验表明，CyGNet的有效性预测未来的事实与重复以及从头事实预测。
英文摘要：
Large knowledge graphs often grow to store temporal facts that model the dynamic relations or interactions of entities along the timeline. Since such temporal knowledge graphs often suffer from incompleteness, it is important to develop time-aware representation learning models that help to infer the missing temporal facts. While the temporal facts are typically evolving, it is observed that many facts often show a repeated pattern along the timeline, such as economic crises and diplomatic activities. This observation indicates that a model could potentially learn much from the known facts appeared in history. To this end, we propose a new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel timeaware copy-generation mechanism. CyGNet is not only able to predict future facts from the whole entity vocabulary, but also capable of identifying facts with repetition and accordingly predicting such future facts with reference to the known facts in the past. We evaluate the proposed method on the knowledge graph completion task using five benchmark datasets. Extensive experiments demonstrate the effectiveness of CyGNet for predicting future facts with repetition as well as de novo fact prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
