A comprehensive survey of knowledge graph embeddings with literals: Techniques and applications
Genet Asefa Gesese, Russa Biswas, Harald Sack · VBN Forskningsportal (Aalborg Universitet) · 2019
Knowledge Graphs are organized to describe entities from any discipline and the interrelations between them. Apart from facilitating the inter-connectivity of datasets in the LOD cloud, KGs have been used in a variety of applications such as Web search or entity linking, and recently are part of popular search systems and Q&A applications etc. However, the KG applications suffer from high computational and storage cost. Hence, there arises the necessity of having a representation learning of the high dimensional KGs into low dimensional spaces preserving structural as well as relational information. In this study, we conduct a comprehensive survey based on techniques of KG embedding models which consider the structured information of the graph as well as the unstructured information in form of literals such as text, numerical values etc. Furthermore, we address the challenges in their embedding models followed by a discussion on different application scenarios.