Knowledge graph question answering is an important technology in intelligent human–robot interaction, which aims at automatically giving answer to human natural language question with the given knowledge graph. For the multi-relation question with higher variety and complexity, the tokens of the question have different priority for the triples selection in the reasoning steps. Most existing models take the question as a whole and ignore the priority information in it. To solve this problem, we propose question-aware memory network for multi-hop question answering, named QA2MN, to update the attention on question timely in the reasoning process. In addition, we incorporate graph context information into knowledge graph embedding model to increase the ability to represent entities and relations. We use it to initialize the QA2MN model and fine-tune it in the training process. We evaluate QA2MN on PathQuestion and WorldCup2014, two representative datasets for complex multi-hop question answering. The result demonstrates that QA2MN achieves state-of-the-art Hits@1 accuracy on the two datasets, which validates the effectiveness of our model.

知识图谱问答是智能人机交互中的一项重要技术，旨在利用给定的知识图谱自动回答人类自然语言问题。对于多样性和复杂度更高的多关系问题，在推理步骤中，问题的标记对三元组选择具有不同的优先级。大多数现有模型将问题视为一个整体而忽略其中的优先级信息。为了解决这个问题，我们提出了用于多跳问答的问题感知记忆网络QA2MN，以在推理过程中及时更新对问题的关注。此外，我们将图上下文信息合并到知识图嵌入模型中，以增加表示实体和关系的能力。我们使用它来初始化 QA2MN 模型并在训练过程中对其进行微调。我们在 PathQuestion 和 WorldCup2014 上评估 QA2MN，这是两个用于复杂多跳问答的代表性数据集。结果表明 QA2MN 达到了最先进的水平点击@ 1级精度上的两个数据集，这验证了我们的模型的有效性。