大数据2026，Vol.12Issue(2)：97-110,14.DOI:10.11959/j.issn.2096-0271.2025033
基于少样本学习和思维链提示的知识概念抽取方法研究
Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting
摘要
Abstract
Knowledge concept extraction has important application value in the fields of education,medical care,and finance.Knowledge concept extraction is a sub-task of named entity recognition.However,due to the lack of data sets and the particularity of knowledge concept entity types,directly applying general named entity recognition methods to knowledge concept extraction tasks often has poor results.In view of the above challenges,a method based on few-shot learning and chain-of-thought prompting for knowledge concept extraction was proposed,utilizing open-source large language models.Firstly,text representations focusing on entity semantics were trained through contrastive learning,and the relevance of the retrieved few-shot examples was enhanced using the K-nearest neighbors algorithm.Secondly,a method utilizing chain-of-thought prompting was adopted to present the samples,with the aim of improving the reasoning ability of large language models in knowledge concept extraction.Experimental results on multiple datasets demonstrate that the few-shot learning and chain-of-thought prompting for knowledge concept extraction method,on the whole,has shown results superior over existing methods.关键词
知识概念抽取/命名实体识别/大语言模型Key words
knowledge concept extraction/named entity recognition/large language model分类
信息技术与安全科学引用本文复制引用
佘霖琳,熊龙洋,陆雪松..基于少样本学习和思维链提示的知识概念抽取方法研究[J].大数据,2026,12(2):97-110,14.基金项目
国家自然科学基金项目(No.62277017) The National Natural Science Foundation of China(No.62277017) （No.62277017）
