Automatic Construction of Subject Knowledge Graph based on Educational Big Data

Abstract

In this paper, we propose an automatic construction method of subject knowledge graph for educational applications. The subject knowledge graph is constructed based on educational big data by using a bootstrapping strategy to gradually expand knowledge points and connections between them. In this paper two different datasets are used. One is the subject teaching resources such as syllabuses, teaching plans, textbooks and etc., which is used to automatically construct the core of subject knowledge graph so as to reduce the dependence on the manual annotation. Meanwhile the high-quality of subject teaching resources is the guarantee of accuracy of the knowledge graph core. The other dataset is the massive Internet encyclopedia texts, which is used to expand and complete the subject knowledge graph. As to algorithm, this paper utilizes the BERT-BiLSTM-CRF model to automatically identify the subject knowledge points, and then evaluates the relationship between the knowledge points by calculating their semantic similarity, PMI and Normalized Google Distance between them. The experimental results show that BERT-BiLSTM-CRF outperforms the baselines significantly, and the three kinds of relationship evaluation models have achieved good results. Finally, computer science and physics science are taken as examples to construct the subject knowledge graphs successfully, which show the effectiveness of our method.