 Given a KG subgraphof extracted facts , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts .
<meaning-changed>  Given a KG subgraphof extracted facts , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts .
 Given a KG subgraphof extracted facts , HGN is jointly trained to generate embeddings for the subgraph's missing edges to form a "hybrid" subgraph, and filter out task-irrelevant facts .