  Multi-relation question answering (QA) is a challenging task, where given
questions usually require long reasoning chains in KGs that consist of multiple
relations. Recently, methods with explicit multi-step reasoning over KGs have
been prominently used in this task and have demonstrated promising performance.
Examples include methods that perform stepwise label propagation through KG
triples and methods that navigate over KG triples based on reinforcement
learning. A main weakness of these methods is that their reasoning mechanisms
are usually complex and difficult to implement or train. In this paper, we
argue that multi-relation QA can be achieved via end-to-end single-step
implicit reasoning, which is simpler, more efficient, and easier to adopt. We
propose QAGCN -- a Question-Aware Graph Convolutional Network (GCN)-based
method that includes a novel GCN architecture with controlled
question-dependent message propagation for the implicit reasoning. Extensive
experiments have been conducted, where QAGCN achieved competitive and even
superior performance compared to state-of-the-art explicit-reasoning methods.
Our code and pre-trained models are available in the repository:
https://github.com/ruijie-wang-uzh/QAGCN
