Temporal Knowledge graph completion (TKGC) is a crucial task that involves
reasoning at known timestamps to complete the missing part of facts and has
attracted more and more attention in recent years. Most existing methods focus
on learning representations based on graph neural networks while inaccurately
extracting information from timestamps and insufficiently utilizing the implied
information in relations. To address these problems, we propose a novel TKGC
model, namely Pre-trained Language Model with Prompts for TKGC (PPT). We
convert a series of sampled quadruples into pre-trained language model inputs
and convert intervals between timestamps into different prompts to make
coherent sentences with implicit semantic information. We train our model with
a masking strategy to convert TKGC task into a masked token prediction task,
which can leverage the semantic information in pre-trained language models.
Experiments on three benchmark datasets and extensive analysis demonstrate that
our model has great competitiveness compared to other models with four metrics.
Our model can effectively incorporate information from temporal knowledge
graphs into the language models.

本文提出了一种基于预训练语言模型的 TKGC 模型 (PPT)，通过将时间戳之间的间隔转换为不同的提示来提取暗示的语义信息以形成连贯的句子，并使用蒙版策略将 TKGC 任务转换为蒙版标记预测任务，有效地将时间知识图中的信息整合到语言模型中，具有较高的竞争力。