国内大语言模型在学科知识图谱自动标注上的应用——以道德与法治和数学学科为例OA北大核心CSTPCD

With the rapid development of artificial intelligence technology,large language models(LLMs)have demonstrated strong abilities in natural language processing and various knowledge applications.This study examined the application of Chinese large language models in the automatic labelling of knowledge graphs for primary and secondary school subjects in particular compulsory education stage morality and law and high school mathematics.In education,the construction of knowledge graphs is crucial for organizing systemic knowledge.However,traditional knowledge graph methods have problems such as low efficiency and labor-cost consumption in data labelling.This study aimed to solve these problems using LLMs,thereby improving the level of automation and intelligence in the construction of knowledge graphs.Based on the status quo of domestic LLMs,this paper discusses their application in the automatic labelling of subject knowledge graphs.Taking morality and rule of law and mathematics as examples,the relevant methods and experimental results are explained.First,the research background and significance are discussed.Second,the development status of the domestic large language model and automatic labelling technology of the subject knowledge graph are then presented.In the methods and model section,an automatic labelling method based on LLMs is explored to improve its application in a subject knowledge graph.This study also explored the subject knowledge graph model to compare and evaluate the actual effect of the automatic labelling method.In the experiment and analysis section,through the automatic labelling experiments and results analysis of the subjects of morality and law and mathematics,the knowledge graphs of the two disciplines are automatically labeled to achieve high accuracy and efficiency.A series of valuable conclusions are obtained,and the effectiveness and accuracy of the proposed methods are verified.Finally,future research directions are discussed.In general,this study provides a new concept and method for the automatic labelling of subject knowledge graphs,which is expected to promote further developments in related fields.