Question answering system (QAS) can be applied everywhere such as in schools, hospitals, banks, e-commerce websites. A smart QAS that can replace people is what people expect. Therefore, there are a lot of studies to build, develop, and improve QAS. However, QAS used for low-resource languages like Vietnamese is still very limited. So, in this paper, we present a method for building Vietnamese QAS. Except for specific Vietnamese language processes, most of our solutions can also be applied to other languages. We build QAS based on knowledge graph (KG) and convolutional neural network (CNN). KG provides knowledge and deducing ability for QAS. CNN is used to classify questions in the natural language to identify the correct answer to a given question. Moreover, we also use distributed architecture to train the CNN model. On the other hands, we also propose a solution to speed up searching for answers in a large KG by partitioning and indexing KG by using the DM-Tree structure. Besides, we also present experimental results and evaluation results of our model using common metrics to prove the effectiveness of our solution.

问答系统（QAS）可以应用于学校、医院、银行、电子商务网站等任何地方。一个可以代替人的智能QAS正是人们所期待的。因此，有很多研究来构建、开发和改进QAS。然而，用于像越南语这样的低资源语言的QAS仍然非常有限。因此，在本文中，我们提出了一种构建越南 QAS的方法。除了特定的越南语流程外，我们的大部分解决方案也可以应用于其他语言。我们基于知识图谱 ( KG ) 和卷积神经网络 ( CNN )构建QAS）。KG为QAS提供知识和推理能力。CNN用于对自然语言中的问题进行分类，以识别给定问题的正确答案。此外，我们还使用分布式架构来训练CNN模型。在其他的手，我们也提出了一个解决方案，以加快寻找在一个大的答案KG的分区和索引KG使用DM树结构。此外，我们还使用通用指标展示了我们模型的实验结果和评估结果，以证明我们的解决方案的有效性。