 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.
<meaning-changed>  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 a new KG-augmented model: Hybrid Graph Network (HGN) , a neural-symbolic model that reasons over both extracted (human-labeled) and generated facts within the same learned graph structure.