Recent years, Knowledge Graph Embeddings (KGEs) have shown promising
performance on link prediction tasks by mapping the entities and relations from
a Knowledge Graph (KG) into a geometric space and thus have gained increasing
attentions. In addition, many recent Knowledge Graphs involve evolving data,
e.g., the fact (\textit{Obama}, \textit{PresidentOf}, \textit{USA}) is valid
only from 2009 to 2017. This introduces important challenges for knowledge
representation learning since such temporal KGs change over time. In this work,
we strive to move beyond the complex or hypercomplex space for KGE and propose
a novel geometric algebra based embedding approach, GeomE, which uses
multivector representations and the geometric product to model entities and
relations. GeomE subsumes several state-of-the-art KGE models and is able to
model diverse relations patterns. On top of this, we extend GeomE to TGeomE for
temporal KGE, which performs 4th-order tensor factorization of a temporal KG
and devises a new linear temporal regularization for time representation
learning. Moreover, we study the effect of time granularity on the performance
of TGeomE models. Experimental results show that our proposed models achieve
the state-of-the-art performances on link prediction over four commonly-used
static KG datasets and four well-established temporal KG datasets across
various metrics.

本论文使用几何代数和张量分解提出了一种新的知识图谱表示学习方法 GeomE 和 TGeomE，用于解决深度学习中的时序数据问题。结果表明，该方法在四个常用的静态 KG 数据集和四个知名的时间 KG 数据集上实现了最先进的性能。

基于几何代数嵌入的静态和时态知识图完成

Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion

Knowledge Graph Question Answering (KGQA) involves retrieving facts from a
Knowledge Graph (KG) using natural language queries. A KG is a curated set of
facts consisting of entities linked by relations. Certain facts include also
temporal information forming a Temporal KG (TKG). Although many natural
questions involve explicit or implicit time constraints, question answering
(QA) over TKGs has been a relatively unexplored area. Existing solutions are
mainly designed for simple temporal questions that can be answered directly by
a single TKG fact. This paper puts forth a comprehensive embedding-based
framework for answering complex questions over TKGs. Our method termed temporal
question reasoning (TempoQR) exploits TKG embeddings to ground the question to
the specific entities and time scope it refers to. It does so by augmenting the
question embeddings with context, entity and time-aware information by
employing three specialized modules. The first computes a textual
representation of a given question, the second combines it with the entity
embeddings for entities involved in the question, and the third generates
question-specific time embeddings. Finally, a transformer-based encoder learns
to fuse the generated temporal information with the question representation,
which is used for answer predictions. Extensive experiments show that TempoQR
improves accuracy by 25--45 percentage points on complex temporal questions
over state-of-the-art approaches and it generalizes better to unseen question
types.

本文提出了一种基于嵌入感知的方法，名为 'Temporal Question Reasoning' (TempoQR)，用于回答复杂的基于时间的知识图谱问答。实验表明，TempoQR 在复杂的时间问题上比现有的先进方法提高了 25-45 个百分点，并且更好地泛化到看不见的问题类型。

TempoQR: 在知识图谱上进行时间问题推理

TempoQR: Temporal Question Reasoning over Knowledge Graphs

Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs
has been widely explored. However, reasoning over Temporal KG (TKG) that
predicts facts in the future is still far from resolved. The key to predict
future facts is to thoroughly understand the historical facts. A TKG is
actually a sequence of KGs corresponding to different timestamps, where all
concurrent facts in each KG exhibit structural dependencies and temporally
adjacent facts carry informative sequential patterns. To capture these
properties effectively and efficiently, we propose a novel Recurrent Evolution
network based on Graph Convolution Network (GCN), called RE-GCN, which learns
the evolutional representations of entities and relations at each timestamp by
modeling the KG sequence recurrently. Specifically, for the evolution unit, a
relation-aware GCN is leveraged to capture the structural dependencies within
the KG at each timestamp. In order to capture the sequential patterns of all
facts in parallel, the historical KG sequence is modeled auto-regressively by
the gate recurrent components. Moreover, the static properties of entities such
as entity types, are also incorporated via a static graph constraint component
to obtain better entity representations. Fact prediction at future timestamps
can then be realized based on the evolutional entity and relation
representations. Extensive experiments demonstrate that the RE-GCN model
obtains substantial performance and efficiency improvement for the temporal
reasoning tasks on six benchmark datasets. Especially, it achieves up to
11.46\% improvement in MRR for entity prediction with up to 82 times speedup
comparing to the state-of-the-art baseline.

本文提出了一种基于递归演化网络的图卷积网络 (RE-GCN) 的模型，以有效地模拟序列的历史事实，捕捉结构依赖性和时间上邻近事实的时序模式，并根据历史的 KG 序列自回归地建模并一并捕获所有事实的时序模式，从而显著提高了六个基准数据集上的时间推理任务的性能和效率。