We design a global-to-local neural network to encode coarse-grained and fine-grained semantic information of entities.
we learn entity global representations by employing R-GCN (Schlichtkrull et al., 2018) on the created heterogeneous graph, and entity local representations by aggregating multiple mentions of specific entities with multi-head attention (Vaswani et al., 2017).