Knowledge Graphs (KGs) are often generated automatically or manually which lead to KGs being in complete. Recent years have witnessed many studies on link prediction using KG embeddings which is one of the mainstream tasks in KG completion. Most of the existing methods learn the latent representation of the entities and relations whereas only a few of them consider contextual information as well as the textual/numeric descriptions of the entities. This talk will cover deep learning based methods for performing KG completion tasks such as link prediction and entity type prediction.