Knowledge Graph Completion

Knowledge graph completion aims to predict unseen relations or tail entities in a knowledge graph. Specifically, this task processes a set of triples $\{(h, r, t)\} \subseteq E \times R \times E$, where $E$ and $R$ are the sets of entities and relations, respectively, to achieve the prediction of unknown relations between two known entities $(h, ?, t)$, or to predict the tail entity $(h, r, ?)$ given the head entity and query relation. This task is of great significance for enhancing the completeness and accuracy of knowledge graphs and is widely applied in recommendation systems, search engine optimization, and natural language processing, among other fields.