Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations. In addition to the structured information KGs help in facilitating interconnectivity and interoperability between different resources represented in the Linked Data Cloud.KGs have been used in a variety of applications such as entity linking, question answering, recommender systems, etc. However, KG applications suffer from high computational and storage costs. Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information. This paper conducts a survey of KG embedding models which not only consider the structured information contained in the form of entities and relations in a KG but also its unstructured information represented as literals such as text, numerical values, images, etc. Along with a theoretical analysis and comparison of the methods proposed so far for generating KG embeddings with literals, an empirical evaluation of the different methods under identical settings has been performed for the general task of link prediction.

中文翻译：

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关于带有文字的知识图嵌入的调查：哪个模型在文字上更好地链接？

知识图（KG）由关于特定领域的结构化信息以实体和关系的形式组成。除结构化信息外，KG还有助于促进链接数据云中表示的不同资源之间的互连性和互操作性。KG已用于各种应用程序，例如实体链接，问题回答，推荐系统等。高昂的计算和存储成本。因此，需要一种能够将高维KG映射到低维空间（即，嵌入空间），保留结构以及相关信息的表示形式。