  In recent years, Knowledge Graph (KG) development has attracted significant
researches considering the applications in web search, relation prediction,
natural language processing, information retrieval, question answering to name
a few. However, often KGs are incomplete due to which Knowledge Graph
Completion (KGC) has emerged as a sub-domain of research to automatically track
down the missing connections in a KG. Numerous strategies have been suggested
to work out the KGC dependent on different representation procedures intended
to embed triples into a low-dimensional vector space. Given the difficulties
related to KGC, researchers around the world are attempting to comprehend the
attributes of the problem statement. This study intends to provide an overview
of knowledge bases combined with different challenges and their impacts. We
discuss existing KGC approaches, including the state-of-the-art Knowledge Graph
Embeddings (KGE), not only on static graphs but also for the latest trends such
as multimodal, temporal, and uncertain knowledge graphs. In addition,
reinforcement learning techniques are reviewed to model complex queries as a
link prediction problem. Subsequently, we explored popular software packages
for model training and examine open research challenges that can guide future
research.
