华中科技大学学报（自然科学版）2024，Vol.52Issue(11)：37-42,92,7.DOI:10.13245/j.hust.241105
基于概念格层面的知识提取方法及应用
Knowledge level extraction method based on concept lattice and its application
摘要
Abstract
To solve the problem that the existing algorithm of generating concept lattice could only output concepts directly,but could not output each knowledge level and the knowledge contained in the same knowledge level meanwhile,a knowledge level(KL)algorithm was proposed to transform formal context into concept lattice.On a macro level,KL algorithm could visualize the knowledge in the formal context.From the micro point of view,KL algorithm could output the specific knowledge level and the knowledge contained in each knowledge level.By comparing KL algorithm with Nextclosure algorithm,it is found that KL algorithm can process data faster than Nextclosure algorithm in terms of complexity when the object set in formal context is large,and when the size of the attribute set in the formal context is not large,KL algorithm has the same data processing speed as Nextclosure algorithm.In terms of generating Hasse graph and knowledge level,KL algorithm has absolute advantage.Research results show that KL algorithm can provide richer results under the same formal context,which is conducive to the popularization of concept lattice theory.关键词
形式概念分析/概念格/知识层面/Hasse图/知识分层算法Key words
formal concept analysis/concept lattice/knowledge level/Hasse diagram/knowledge level algorithm分类
通用工业技术引用本文复制引用
毛华,刘畅,袁晓垒,刘川..基于概念格层面的知识提取方法及应用[J].华中科技大学学报（自然科学版）,2024,52(11):37-42,92,7.基金项目
国家自然科学基金青年科学基金资助项目(12202130) （12202130）
河北省自然科学基金资助项目(A2022201034). （A2022201034）
