Knowledge graphs (KGs) have become effective knowledge resources in diverse
applications, and knowledge graph embedding (KGE) methods have attracted
increasing attention in recent years. However, it's still challenging for
conventional KGE methods to handle unseen entities or relations during the
model test. Much effort has been made in various fields of KGs to address this
problem. In this paper, we use a set of general terminologies to unify these
methods and refer to them as Knowledge Extrapolation. We comprehensively
summarize these methods classified by our proposed taxonomy and describe their
correlations. Next, we introduce the benchmarks and provide comparisons of
these methods from aspects that are not reflected by the taxonomy. Finally, we
suggest some potential directions for future research.

本文提出知识外推的概念，对知识图谱嵌入方法分类并详细总结及比较，探讨其在处理未知实体和关系方面的挑战，并提出未来的研究方向。

面向未见元素的泛化：知识图谱知识外推综述

Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs

Human can extrapolate well, generalize daily knowledge into unseen scenarios,
raise and answer counterfactual questions. To imitate this ability via
generative models, previous works have extensively studied explicitly encoding
Structural Causal Models (SCMs) into architectures of generator networks. This
methodology, however, limits the flexibility of the generator as they must be
carefully crafted to follow the causal graph, and demands a ground truth SCM
with strong ignorability assumption as prior, which is a nontrivial assumption
in many real scenarios. Thus, many current causal GAN methods fail to generate
high fidelity counterfactual results as they cannot easily leverage
state-of-the-art generative models. In this paper, we propose to study
counterfactual synthesis from a new perspective of knowledge extrapolation,
where a given knowledge dimension of the data distribution is extrapolated, but
the remaining knowledge is kept indistinguishable from the original
distribution. We show that an adversarial game with a closed-form discriminator
can be used to address the knowledge extrapolation problem, and a novel
principal knowledge descent method can efficiently estimate the extrapolated
distribution through the adversarial game. Our method enjoys both elegant
theoretical guarantees and superior performance in many scenarios.

这篇论文提出了一种基于知识外推的对因果推断的新方法，通过对抗训练生成对抗网络，即可实现对难以使用传统因果图构建的复杂数据分布的可解释性推断，并在很多场景下取得了优异的性能表现。

基于 GAN 的知识推广原则

Principled Knowledge Extrapolation with GANs

We study the knowledge extrapolation problem to embed new components (i.e.,
entities and relations) that come with emerging knowledge graphs (KGs) in the
federated setting. In this problem, a model trained on an existing KG needs to
embed an emerging KG with unseen entities and relations. To solve this problem,
we introduce the meta-learning setting, where a set of tasks are sampled on the
existing KG to mimic the link prediction task on the emerging KG. Based on
sampled tasks, we meta-train a graph neural network framework that can
construct features for unseen components based on structural information and
output embeddings for them. Experimental results show that our proposed method
can effectively embed unseen components and outperforms models that consider
inductive settings for KGs and baselines that directly use conventional KG
embedding methods.

本研究探讨在联邦环境下，如何应对新出现的知识图谱中包含的新组件（实体和关系），并提出了一种基于元学习的图神经网络框架，可以根据结构信息构建未见过的组件特征，并嵌入这些组件。实验结果表明，我们的方法可以有效地嵌入未见过的组件，并胜过考虑 KG 归纳设置和直接使用传统 KG 嵌入方法的基线模型。