Educational knowledge graph based intelligent question answering for automatic control disciplines

Abstract

With the further development of education informatization, Educational Knowledge Graph (EKG) based intelligent Question Answering (KGQA) has attracted significant attention in smart education. However, current educational KGQA faces enormous challenges, such as the incomplete questions from students, the dispersed knowledge from EKG, and the scarce and imbalanced dataset. In this paper, a novel educational KGQA model was proposed for answering student’s questions on automatic control disciplines. Firstly, a topic entity detection algorithm was constructed based on BERT-BiLSTM-CRF and domain dictionary, and an intention recognition algorithm was built based on BERT and TextCNN to accurately locate the topic entity by formulating entity priority, entity completion rules, and similarity calculation. Then, a custom weighted cross-entropy loss function (CCL) was designed to alleviate the influence of imbalanced samples in the training dataset on the model classifier. In addition, the first Chinese dataset for educational KGQA in automatic control disciplines (ACKGQA) was constructed. Finally, extensive experiments are performed to evaluate the effectiveness and generalizations of the proposed KGQA model on the ACKGQA dataset and five benchmark public datasets. The proposed KGQA obtains the recognition precision of 87.5% and the recall of 86.25% on the ACKGQA dataset and exhibits better overall performance on other five benchmark datasets. Experimental results demonstrate that our educational KGQA model can achieve outstanding performance when facing the challenges posed by imbalanced datasets inherent in educational knowledge graphs.