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Recently, several studies have explored methods for using KG embedding toanswer logical queries. These approaches either treat embedding learning andquery answering as two separated learning tasks, or fail to deal with thevariability of contributions from different query paths. We proposed toleverage a graph attention mechanism to handle the unequal contribution ofdifferent query paths. However, commonly used graph attention assumes that thecenter node embedding is provided, which is unavailable in this task since thecenter node is to be predicted. To solve this problem we propose a multi-headattention-based end-to-end logical query answering model, called ContextualGraph Attention model(CGA), which uses an initial neighborhood aggregationlayer to generate the center embedding, and the whole model is trained jointlyon the original KG structure as well as the sampled query-answer pairs. We alsointroduce two new datasets, DB18 and WikiGeo19, which are rather large in sizecompared to the existing datasets and contain many more relation types, and usethem to evaluate the performance of the proposed model. Our result shows thatthe proposed CGA with fewer learnable parameters consistently outperforms thebaseline models on both datasets as well as Bio dataset.

Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs