Stemming from traditional knowledge graphs (KGs), hyper-relational KGs (HKGs)
provide additional key-value pairs (i.e., qualifiers) for each KG fact that
help to better restrict the fact validity. In recent years, there has been an
increasing interest in studying graph reasoning over HKGs. In the meantime, due
to the ever-evolving nature of world knowledge, extensive parallel works have
been focusing on reasoning over temporal KGs (TKGs), where each TKG fact can be
viewed as a KG fact coupled with a timestamp (or time period) specifying its
time validity. The existing HKG reasoning approaches do not consider temporal
information because it is not explicitly specified in previous benchmark
datasets. Besides, all the previous TKG reasoning methods only lay emphasis on
temporal reasoning and have no way to learn from qualifiers. To this end, we
aim to fill the gap between TKG reasoning and HKG reasoning. We develop two new
benchmark hyper-relational TKG (HTKG) datasets, i.e., Wiki-hy and YAGO-hy, and
propose a HTKG reasoning model that efficiently models both temporal facts and
qualifiers. We further exploit additional time-invariant relational knowledge
from the Wikidata knowledge base and study its effectiveness in HTKG reasoning.
Time-invariant relational knowledge serves as the knowledge that remains
unchanged in time (e.g., Sasha Obama is the child of Barack Obama), and it has
never been fully explored in previous TKG reasoning benchmarks and approaches.
Experimental results show that our model substantially outperforms previous
related methods on HTKG link prediction and can be enhanced by jointly
leveraging both temporal and time-invariant relational knowledge.

我们填补了时态图谱推理和超关系时态图谱推理之间的差距，通过开发两个新的基准数据集以及提出一个高效模型，同时建模时间事实和限定词，并探索从 Wikidata knowledge base 中获取的额外的不随时间变化的关系知识对于高效推理的有效性。

探索在加强了时间不变关系知识的超关系临时知识图中的链接预测

Exploring Link Prediction over Hyper-Relational Temporal Knowledge  Graphs Enhanced with Time-Invariant Relational Knowledge

In the last few years, the solution to Knowledge Graph (KG) completion via
learning embeddings of entities and relations has attracted a surge of
interest. Temporal KGs(TKGs) extend traditional Knowledge Graphs (KGs) by
associating static triples with timestamps forming quadruples. Different from
KGs and TKGs in the transductive setting, constantly emerging entities and
relations in incomplete TKGs create demand to predict missing facts with unseen
components, which is the extrapolation setting. Traditional temporal knowledge
graph embedding (TKGE) methods are limited in the extrapolation setting since
they are trained within a fixed set of components. In this paper, we propose a
Meta-Learning based Temporal Knowledge Graph Extrapolation (MTKGE) model, which
is trained on link prediction tasks sampled from the existing TKGs and tested
in the emerging TKGs with unseen entities and relations. Specifically, we
meta-train a GNN framework that captures relative position patterns and
temporal sequence patterns between relations. The learned embeddings of
patterns can be transferred to embed unseen components. Experimental results on
two different TKG extrapolation datasets show that MTKGE consistently
outperforms both the existing state-of-the-art models for knowledge graph
extrapolation and specifically adapted KGE and TKGE baselines.

本文介绍了一种基于元学习的时间知识图谱外推模型（MTKGE），该模型在已知时间知识图谱中进行链接预测任务的元训练，并在具有未知实体和关系的新兴时间知识图谱中进行测试。通过捕获关系的相对位置模式和时间序列模式的 GNN 框架，学习模式的嵌入可以转移到未知组件。实验结果表明 MTKGE 模型在两个不同的时间知识图谱外推数据集上均优于现有的知识图谱外推和特定自适应 KGE 和 TKGE 基线的最新模型。

基于元学习的时态知识图谱推理

Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph

Knowledge Graph (KG) completion has been excessively studied with a massive
number of models proposed for the Link Prediction (LP) task. The main
limitation of such models is their insensitivity to time. Indeed, the temporal
aspect of stored facts is often ignored. To this end, more and more works
consider time as a parameter to complete KGs. In this paper, we first
demonstrate that, by simply increasing the number of negative samples, the
recent AttH model can achieve competitive or even better performance than the
state-of-the-art on Temporal KGs (TKGs), albeit its nontemporality. We further
propose Hercules, a time-aware extension of AttH model, which defines the
curvature of a Riemannian manifold as the product of both relation and time.
Our experiments show that both Hercules and AttH achieve competitive or new
state-of-the-art performances on ICEWS04 and ICEWS05-15 datasets. Therefore,
one should raise awareness when learning TKGs representations to identify
whether time truly boosts performances.

在 Knowledge Graph completion 中，本文提出增加负例样本数量的方法被证明可以让不考虑时间因素的 AttH 模型在 Temporal KGs 上取得比现有最优解甚至更好的表现，同时提出了考虑时间因素的 Hercules 模型，取得了竞争性或全新最优的表现。因此，在学习 Temporal KGs 时，需要注意时间因素是否真正提高了性能表现。