  Graph Attention Network (GAT) focuses on modelling simple undirected and
single relational graph data only. This limits its ability to deal with more
general and complex multi-relational graphs that contain entities with directed
links of different labels (e.g., knowledge graphs). Therefore, directly
applying GAT on multi-relational graphs leads to sub-optimal solutions. To
tackle this issue, we propose r-GAT, a relational graph attention network to
learn multi-channel entity representations. Specifically, each channel
corresponds to a latent semantic aspect of an entity. This enables us to
aggregate neighborhood information for the current aspect using relation
features. We further propose a query-aware attention mechanism for subsequent
tasks to select useful aspects. Extensive experiments on link prediction and
entity classification tasks show that our r-GAT can model multi-relational
graphs effectively. Also, we show the interpretability of our approach by case
study.
