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Knowledge Graphs (KGs) are composed of structured information about aparticular domain in the form of entities and relations. In addition to thestructured information KGs help in facilitating interconnectivity andinteroperability between different resources represented in the Linked DataCloud. KGs have been used in a variety of applications such as entity linking,question answering, recommender systems, etc. However, KG applications sufferfrom high computational and storage costs. Hence, there arises the necessityfor a representation able to map the high dimensional KGs into low dimensionalspaces, i.e., embedding space, preserving structural as well as relationalinformation. This paper conducts a survey of KG embedding models which not onlyconsider the structured information contained in the form of entities andrelations in a KG but also the unstructured information represented as literalssuch as text, numerical values, images, etc. Along with a theoretical analysisand comparison of the methods proposed so far for generating KG embeddings withliterals, an empirical evaluation of the different methods under identicalsettings has been performed for the general task of link prediction.

A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?