基于包含度和形式概念分析抽取模糊语言命题的中期报告摘要：本文采用形式概念分析和包含度模型相结合的方法抽取模糊语言命题，并探讨其在知识表示和推理中的应用。具体步骤包括：（1）基于语料库建立基本命题集；（2）利用包含度模型计算命题之间的相似度；（3）运用形式概念分析将命题集合归纳为概念层次结构；（4）从概念层次结构中抽取模糊语言命题。实验结果表明，该方法可以有效地抽取模糊语言命题，并具有较高的准确性和可解释性。关键词：形式概念分析；包含度模型；模糊语言命题；知识表示；知识推理Abstract:Thispaperproposesamethodofextractingfuzzylanguagepropositionsusingthecombinationofformalconceptanalysisandinclusiondegreemodel,anddiscussesitsapplicationinknowledgerepresentationandreasoning.Thespecificstepsinclude:(1)establishingabasicpropositionsetbasedonthecorpus;(2)calculatingthesimilaritybetweenpropositionsusingtheinclusiondegreemodel;(3)usingformalconceptanalysistoinducethepropositionsetintoahierarchicalstructureofconcepts;(4)extractingfuzzylanguagepropositionsfromthehierarchicalstructureofconcepts.Theexperimentalresultsshowthattheproposedmethodcaneffectivelyextractfuzzylanguagepropositionswithhighaccuracyandinterpretability.Keywords:formalconceptanalysis;inclusiondegreemodel;fuzzylanguageproposition;knowledgerepresentation;knowledgereasoning