摘要
针对现有本体诊断工具返回最小诊断数量多、用户无法科学取舍的问题,首先分析现有筛选诊断方法存在问题,然后结合应用本体及其需求特点,探讨将原本相互孤立的、分别应用在不同诊断工具中的公理可信度筛选诊断法、信息熵筛选诊断法之核心技术进行组合、优化的思路与方法。在此基础上,提出一个综合性的启发式筛选诊断法,阐述其基本思路与操作步骤,并以典型实验本体为例进一步验证方法的有效性。
Considering the problems of existing ontology diagnosis methods on returning too many minimal diagnosis and placing too much burden on users,and combining with application ontology and its requirement characteristics,the article explored new research ideas and methods to combine and optimize the core algorithms of axiom credibility and information entropy filtering and diagnosis methods applied in different diagnostic tools respectively.Then it put forward a comprehensive heuristic filtering and diagnosis method and expounded its basic idea and operation steps,and used typical experimental ontology to validate the effectiveness of this method.
出处
《农业图书情报学刊》
2016年第2期9-18,共10页
Journal of Library and Information Sciences in Agriculture
基金
2013年国家社科基金青年项目"数字图书馆用户数据资源化管理研究"(项目编号:13CTQ012)
关键词
应用本体
最小诊断
公理可信度
信息熵
Application ontology
Minimal diagnosis
Axiom credibility
Information entropy