Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in
Knowledge Graphs for future timesteps based on a history of Knowledge Graphs.
To this day, standardized evaluation protocols and rigorous comparison across
TKG models are available, but the importance of simple baselines is often
neglected in the evaluation, which prevents researchers from discerning actual
and fictitious progress. We propose to close this gap by designing an intuitive
baseline for TKG Forecasting based on predicting recurring facts. Compared to
most TKG models, it requires little hyperparameter tuning and no iterative
training. Further, it can help to identify failure modes in existing
approaches. The empirical findings are quite unexpected: compared to 11 methods
on five datasets, our baseline ranks first or third in three of them, painting
a radically different picture of the predictive quality of the state of the
art.

基于历史知识图谱，通过预测重复事实，在预测未来时间步内的知识图谱的链接方面提出了一个直观的 TKG 预测基线，与大多数 TKG 模型相比，它需要较少的超参数调整和无迭代训练，并且能够帮助识别现有方法中的失败模式。与五个数据集上的 11 种方法相比，我们的基线在其中三个数据集中排名第一或第三，展现了现有技术在预测质量上的截然不同的画面。

历史重演：时间知识图谱预测的基础

History repeats itself: A Baseline for Temporal Knowledge Graph  Forecasting

Temporal Knowledge Graph (TKG) forecasting aims to predict future facts based
on given histories. Most recent graph-based models excel at capturing
structural information within TKGs but lack semantic comprehension abilities.
Nowadays, with the surge of LLMs, the LLM-based TKG prediction model has
emerged. However, the existing LLM-based model exhibits three shortcomings: (1)
It only focuses on the first-order history for prediction while ignoring
high-order historical information, resulting in the provided information for
LLMs being extremely limited. (2) LLMs struggle with optimal reasoning
performance under heavy historical information loads. (3) For TKG prediction,
the temporal reasoning capability of LLM alone is limited. To address the first
two challenges, we propose Chain-of-History (CoH) reasoning which explores
high-order histories step-by-step, achieving effective utilization of
high-order historical information for LLMs on TKG prediction. To address the
third issue, we design CoH as a paly-and-plug module to enhance the performance
of graph-based models for TKG prediction. Extensive experiments on three
datasets and backbones demonstrate the effectiveness of CoH.

基于 LLM 的时态知识图预测模型存在三个缺点，为解决这些问题，我们提出了一种称为 CoH 的历史链推理方法，以实现对高阶历史信息的有效利用，进而增强了基于图模型的 TKG 预测性能。