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 the 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.

本文对考虑包括文本、数字、图像等非结构化信息的 KG 嵌入模型进行了综述和对比，同时在相同条件下对链接预测的不同方法进行了实证评估。