Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (10): 1880-1887.DOI: 10.3778/j.issn.1673-9418.2106093

• Knowledge-Based Question Answering Systems • Previous Articles     Next Articles

Question-aware Graph Convolutional Network for Educational Knowledge Base Question Answering

LIN Qika, ZHANG Lingling, LIU Jun, ZHAO Tianzhe   

  1. 1. School of Computer Science and Technology, Xi??an Jiaotong University, Xi??an 710049, China
    2. Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology R&D, Xi??an 710049, China
  • Online:2021-10-01 Published:2021-09-30



  1. 1. 西安交通大学 计算机科学与技术学院,西安 710049
    2. 陕西省天地网技术重点实验室,西安 710049


In recent years, with the continuous informatization of education and the accumulation of massive education resources and teaching data, some educational knowledge bases have been proposed, which provides a good deve-lopment condition for data-driven intelligent education. The question answering method based on educational know-ledge base can provide learners with instant tutoring, and then effectively improve their learning interest and efficiency. However, there are few studies on educational knowledge base question answering (KBQA), and most of the open domain KBQA methods independently model question sentences and candidate answer entities, so the effect of modeling is limited. Based on this, this paper proposes a question answering method of educational knowledge base based on question-aware graph convolutional network (GCN). Firstly, for a specific question, the description information and query entity set of the question are extracted. And they are processed respectively by Transformer and pre-trained embeddings of the knowledge base. Secondly, the subgraph of candidate answer set is extracted from the knowledge base according to the query entity set, and the node information is updated by the GCN with two attention mechanisms. The scores of attention are expressed by the question description and the query entity set respectively. In this way, the question-aware GCN is realized. Finally, the query description information, query entity set and candidate entity representation are fused to calculate the score and predict the answer. Experiments are carried out on the real data set MOOC Q&A, and the prediction accuracy and mean reciprocal rank are used to evaluate. The experimental results show that the proposed method is superior to the benchmark models.

Key words: graph convolutional network (GCN), attention, educational knowledge base, knowledge base question answering (KBQA), knowledge graph


近年来,随着教育信息化的不断深入,海量教育资源和教学数据不断累积,一些教育知识库被提出,这为数据驱动的智慧教育提供了良好的发展条件。基于教育知识库的问答方法能够为学习者提供即时的答疑辅导,进而有效提升学习者的学习兴趣和效率。然而,目前特定于教育领域的知识库问答研究较少,且开放领域的知识库问答方法大多独立地建模问句和候选答案实体,因而建模效果有限。基于此,提出一种基于问句感知图卷积网络的教育知识库问答方法。首先,针对特定问句,提取其中的问句描述信息和查询实体集,并分别通过Transformer和预训练的知识库嵌入进行处理得到两者的表示;其次,根据查询实体集从知识库中抽取候选答案集的子图,并通过双注意力的图卷积神经网络更新节点信息,其中注意力的得分分别利用问句描述信息和查询实体集的表示,进而实现问句感知;最后,融合问句描述信息、查询实体集和候选实体表示来计算得分,并预测答案。在真实数据集MOOC Q&A上进行实验,采用预测准确率和平均倒数排名的指标进行评估,实验结果表明提出的方法优于基准模型。

关键词: 图卷积网络(GCN), 注意力, 教育知识库, 知识库问答(KBQA), 知识图谱