 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 .
<clarity>  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 complementary facts, encode relational information in the resulting "hybrid" graph, then reason over the hybrid graph while filtering out context-irrelevant edges .