Knowledge Graph (KG) is a graph based data structure to represent facts of
the world where nodes represent real world entities or abstract concept and
edges represent relation between the entities. Graph as representation for
knowledge has several drawbacks like data sparsity, computational complexity
and manual feature engineering. Knowledge Graph embedding tackles the drawback
by representing entities and relation in low dimensional vector space by
capturing the semantic relation between them. There are different KG embedding
models. Here, we discuss translation based and neural network based embedding
models which differ based on semantic property, scoring function and
architecture they use. Further, we discuss application of KG in some domains
that use deep learning models and leverage social media data.

知识图谱是一个基于图形的数据结构，用于表示世界的事实，其中节点表示现实世界的实体或抽象概念，边表示实体之间的关系。知识图谱嵌入通过在低维向量空间中表示实体和关系来捕捉它们之间的语义关系。本文讨论了基于翻译和神经网络的嵌入模型，它们根据语义属性、评分函数和架构的不同而有所区别，并进一步讨论了在一些使用深度学习模型和利用社交媒体数据的领域中应用知识图谱。

知识图谱嵌入模型及其应用调查

Survey on Embedding Models for Knowledge Graph and its Applications

Knowledge graphs are inherently incomplete. Therefore substantial research
has been directed toward knowledge graph completion (KGC), i.e., predicting
missing triples from the information represented in the knowledge graph (KG).
KG embedding models (KGEs) have yielded promising results for KGC, yet any
current KGE is incapable of: (1) fully capturing vital inference patterns
(e.g., composition), (2) capturing prominent patterns jointly (e.g., hierarchy
and composition), and (3) providing an intuitive interpretation of captured
patterns. In this work, we propose ExpressivE, a fully expressive
spatio-functional KGE that solves all these challenges simultaneously.
ExpressivE embeds pairs of entities as points and relations as
hyper-parallelograms in the virtual triple space $\mathbb{R}^{2d}$. This model
design allows ExpressivE not only to capture a rich set of inference patterns
jointly but additionally to display any supported inference pattern through the
spatial relation of hyper-parallelograms, offering an intuitive and consistent
geometric interpretation of ExpressivE embeddings and their captured patterns.
Experimental results on standard KGC benchmarks reveal that ExpressivE is
competitive with state-of-the-art KGEs and even significantly outperforms them
on WN18RR.

研究知识图谱的不完整性，并提出了一种旨在解决知识图谱完成中的挑战、同时呈现出直观的图形化解释的 KG 嵌入模型 - ExpressivE。