Knowledge-graph completion methods infer missing relations from existing graph structure.

Knowledge graphs are commonly incomplete because they are assembled automatically from heterogeneous data. Relation-reasoning systems address this by searching paths between entity pairs and combining path information to infer candidate links. The VRNet study frames this as graph completion and uses variational inference with reinforcement learning to search relevant paths rather than traverse every possible route. Such predictions remain probabilistic hypotheses, not guaranteed facts, and require evaluation against held-out relations.