计算机应用与软件2018，Vol.35Issue(3)：219-224,303,7.DOI:10.3969/j.issn.1000-386x.2018.03.042
基于粗糙FCA-概念代数的上下文本体建模
CONTEXT ONTOLOGY MODELING BASED ON ROUGH FCA-CONCEPT ALGEBRA
摘要
Abstract
In the contextual ontology model,new knowledge is derived from existing contextual information,but there are two problems in reasoning:(1)The existing context may imply a lot of useful information,but the existing method does not deal with this point before the reasoning, the context is incomplete and the reasoned knowledge may not be comprehensive.(2)After the reasoning, some issues make the ontology may not have better scalability, such as Emergence of new knowledge;new knowledge and old knowledge are not coordinated and so on.Aiming at the above two problems,this paper draw on the rough processing method of rough FCA, and proposed to extract the implicit context based on coarse FCA context extraction method.Then the concept algebra was used to formally represent the depth of all the obtained contexts and built a concept network with better scalability.Experimental results showed that the accuracy of context inference based on the proposed method was higher than that of direct inference,and it had obvious advantages in ontology scalability.关键词
粗糙FCA/概念代数/上下文本体/概念网/不完整性/可扩展性Key words
Rough FCA/Concept algebra/Context ontology/Concept network/Incompleteness/Extensibility分类
信息技术与安全科学引用本文复制引用
安敬民,李冠宇..基于粗糙FCA-概念代数的上下文本体建模[J].计算机应用与软件,2018,35(3):219-224,303,7.基金项目
国家自然科学基金项目(61371090,61602076). （61371090,61602076）
