A review of Knowledge graph link prediction using graph neural networks

Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). In order to use KGs in downstream tasks it is desirable to predict missing links in KGs. In this review we will study the current approaches and the challenges exists..