Recently Graph convolutional networks (GCNs) have gained significant interest in the deep learning world to solve problems with knowledge graphs (KGs) containing nodes, edges and relationships. Training a model to solve a problem with graph data is complex due to its multi-relational nature, types of relations, nodes, attributes and also noise when extracted from textual sources using natural language processing (NLP). Primarily two different kind of problems have been studied ...

Knowledge graphs are being used in the field of machine learning for various applications including question & answering, link prediction, fact checking, entity disambiguation etc. For many of these applications finding the missing relationships in the graph is important to ensure, completeness, correctness and quality. This involves the task of entity prediction and relationship prediction. Basically a knowledge graph is a collection of entities and relationships between them in the form ...