Editorial of the Special Issue on Deep Learning and Knowledge Graphs

Over the past years, there has been a rapid growth in the use and the importance of Knowledge Graphs (KGs) along with their application to many important tasks such as entity linking, recommender systems, etc. KGs are large networks of real-world entities described in terms of their semantic types and their relationships to each other. On the other hand, Deep Learning has also become an important area of research, achieving important breakthroughs in various research fields, especially Natural Language Processing (NLP) and Image Processing. Consequently, in recent years there have been several studies that combine Deep Learning methods with KGs. For example 1) knowledge representation learning techniques aimed at embedding entities and relations in a KG into a dense and low-dimensional vector space, 2) relation extraction techniques, aimed at extracting facts and relations from the text for automatically generating KGs, 3) entity linking techniques, aimed at completing KGs, 4) using KGs as an additional prior for image recognition, etc.