Representation and Extraction of Physics Knowledge Based on Knowledge Graph and Embedding-Combined Text Classification for Cooperative Learning

Abstract

Physical knowledge is the foundation of most engineering fields in particular such as product design, analysis, and operation and maintenance. However, due to the complexity of physical concepts, laws, and calculations, students can be easily overwhelmed by the conceptual ideas in the process of learning physics. This paper proposes a new way for helping students grasp the logical relation between the physics knowledge points based on neural networks and knowledge graph technology. Specifically, we use Python scripts to collect the articles about physics knowledge on the Internet as the raw data. After removing the special characters and other irrelevant text, the rest of the data is passed to several neural networks based on BERT and ERNIE for their effective and efficient classification into seven kinds of physics knowledge. The experimental results show that using ERNIE-BERT for embedding and using RCNN for the downstream model achieve the best performance. Knowledge graph is used to build a tree structure of physics knowledge, holding the physics knowledge picked out by the neural networks under corresponding nodes.