Temporal Knowledge Graph Reasoning (TKGR) is the task of inferring missing
facts for incomplete TKGs in complex scenarios (e.g., transductive and
inductive settings), which has been gaining increasing attention. Recently, to
mitigate dependence on structured connections in TKGs, text-based methods have
been developed to utilize rich linguistic information from entity descriptions.
However, suffering from the enormous parameters and inflexibility of
pre-trained language models, existing text-based methods struggle to balance
the textual knowledge and temporal information with computationally expensive
purpose-built training strategies. To tap the potential of text-based models
for TKGR in various complex scenarios, we propose ChapTER, a Contrastive
historical modeling framework with prefix-tuning for TEmporal Reasoning.
ChapTER feeds history-contextualized text into the pseudo-Siamese encoders to
strike a textual-temporal balance via contrastive estimation between queries
and candidates. By introducing virtual time prefix tokens, it applies a
prefix-based tuning method to facilitate the frozen PLM capable for TKGR tasks
under different settings. We evaluate ChapTER on four transductive and three
few-shot inductive TKGR benchmarks, and experimental results demonstrate that
ChapTER achieves superior performance compared to competitive baselines with
only 0.17% tuned parameters. We conduct thorough analysis to verify the
effectiveness, flexibility and efficiency of ChapTER.

ChapTER 是一种基于对比的历史建模框架，通过引入前缀调整方法来在不同设置下为 TEmporal Reasoning 任务提供计算能力，实现了对比估计，从而在各种复杂场景下发挥了基于文本的模型的潜力。