 To address these issues, we propose  Hybrid Graph Network (HGN) , a neural-symbolic model that reasons over both extracted (human-labeled) and generated facts within the same learned graph structure.
<clarity>  To address these issues, we propose  Hybrid Graph Network (HGN) , a neural-symbolic model that reasons over both extracted (human-labeled) and generated facts within the same learned graph structure.
 To address these issues, we propose Hybrid Graph Network (HGN) . Unlike prior methods, HGN learns to jointly contextualize extracted and generated knowledge by reasoning over both within a unified graph structure.