Research and Application of Mathematical Knowledge Graph Based on Ontology Learning

Abstract

The knowledge graph has the potential to provide strong data support for systems such as those that automatically make recommendations and answer intelligent questions. An ontology learning (OL) approach is provided for use in the building of a junior high school mathematics knowledge map in an effort to address the challenge posed by the use of a single data source in the construction of an existing domestic subject knowledge map. Learners receive learning support services that are both timely and intelligent through the realization of an intelligent question answering and automatic recommendation system based on the mathematics knowledge map of junior high school. This not only offers a potential solution to the issue of personalization in junior high school mathematics online instruction, but also offers an idea for how the issue might be resolved. In comparison to a number of traditional benchmark systems, the methodology presented in this paper demonstrates marked improvements in terms of accuracy, precision, recall, and F value in the tests conducted on the dataset that was self-constructed to measure junior high school mathematics knowledge recommendation. The suggested method can recommend knowledge points to learners with more accuracy and help them develop a knowledge system, both of which are expected to provide support for the adaptive and personalized learning of learners.