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

Although the great success of open-domain dialogue generation, unseen
entities can have a large impact on the dialogue generation task. It leads to
performance degradation of the model in the dialog generation. Previous
researches used retrieved knowledge of seen entities as the auxiliary data to
enhance the representation of the model. Nevertheless, logical explanation of
unseen entities remains unexplored, such as possible co-occurrence or
semantically similar words of them and their entity category. In this work, we
propose an approach to address the challenge above. We construct a graph by
extracting entity nodes in them, enhancing the representation of the context of
the unseen entity with the entity's 1-hop surrounding nodes. Furthermore, We
added the named entity tag prediction task to apply the problem that the unseen
entity does not exist in the graph. We conduct our experiments on an open
dataset Wizard of Wikipedia and the empirical results indicate that our
approach outperforms the state-of-the-art approaches on Wizard of Wikipedia.

本研究提出了一种基于图形的方法，利用实体节点和命名实体标签预测来加强上下文中未见实体的表示，以解决在对话生成中未见实体的逻辑解释问题，并通过实验表明该方法在开放数据集 Wizard of Wikipedia 上优于现有方法。

GE-Blender: Blender 的基于图的知识增强

GE-Blender: Graph-Based Knowledge Enhancement for Blender

We present the zero-shot entity linking task, where mentions must be linked
to unseen entities without in-domain labeled data. The goal is to enable robust
transfer to highly specialized domains, and so no metadata or alias tables are
assumed. In this setting, entities are only identified by text descriptions,
and models must rely strictly on language understanding to resolve the new
entities. First, we show that strong reading comprehension models pre-trained
on large unlabeled data can be used to generalize to unseen entities. Second,
we propose a simple and effective adaptive pre-training strategy, which we term
domain-adaptive pre-training (DAP), to address the domain shift problem
associated with linking unseen entities in a new domain. We present experiments
on a new dataset that we construct for this task and show that DAP improves
over strong pre-training baselines, including BERT. The data and code are
available at this https URL

本研究提出了零样本实体链接任务，通过大规模无标记数据预训练的强阅读理解模型与适应性预训练策略，成功实现了无先验标注数据的实体链接，并在新数据集上获得了比 BERT 等强预训练模型更好的表现。