  Recently, several studies have explored methods for using KG embedding to
answer logical queries. These approaches either treat embedding learning and
query answering as two separated learning tasks, or fail to deal with the
variability of contributions from different query paths. We proposed to
leverage a graph attention mechanism to handle the unequal contribution of
different query paths. However, commonly used graph attention assumes that the
center node embedding is provided, which is unavailable in this task since the
center node is to be predicted. To solve this problem we propose a multi-head
attention-based end-to-end logical query answering model, called Contextual
Graph Attention model(CGA), which uses an initial neighborhood aggregation
layer to generate the center embedding, and the whole model is trained jointly
on the original KG structure as well as the sampled query-answer pairs. We also
introduce two new datasets, DB18 and WikiGeo19, which are rather large in size
compared to the existing datasets and contain many more relation types, and use
them to evaluate the performance of the proposed model. Our result shows that
the proposed CGA with fewer learnable parameters consistently outperforms the
baseline models on both datasets as well as Bio dataset.
