面向知识发现的模糊本体融合与推理模型研究

Study on Knowledge Discovery Model Based on Fuzzy Ontology Fusion and Reasoning

现有知识发现研究难以兼顾不同领域知识的精准性与模糊性,也缺乏描述和定义模糊本体的语言工具,本文从知识模糊性角度出发,提出一种基于OWL(ontology web language)语言的模糊本体表现模型,通过SWRL(semantic web rule language)语言表示精确规则和模糊规则,并结合概念对和隶属度将模糊知识转换成精确知识实现本体融合推理,构建面向知识发现的模糊本体融合和推理模型。选取药物相互作用这一典型领域的Drugs与Drugbank数据库中肿瘤及精神卫生疾病相关的药物数据对模型进行验证,研究结果表明,可在保持准确率水平的情况下,将对药物相互作用知识发现尤为重要的召回率显著提高至89.94%。本文提出的模糊本体模型可以同时描述精确知识和模糊知识,简化了对模糊知识的表示和处理。

Existing research on knowledge discovery struggles to balance the accuracy and ambiguity of knowledge in different fields,and lacks the language and tools to describe and define fuzzy ontology.This study proposed a fuzzy ontology representation model based on OWL(ontology web language)language from the perspective of knowledge ambiguity,and expressed precise rules and fuzzy rules using SWRL language.Combining the concept pair and membership degree,the fuzzy knowledge was transformed into precise knowledge to realize ontology fusion reasoning,and a fuzzy ontology fusion and reasoning model oriented to knowledge discovery was constructed.The drug data related to tumor or mental disease in Drugs and DrugBank databases were selected to verify the model.The results showed that the recall rate,which is particularly important for drug interaction knowledge discovery,can be significantly increased to 89.94%while maintaining the accuracy level.The fuzzy ontology model proposed in this study can describe both accurate knowledge and fuzzy knowledge,simplifying the representation and processing of fuzzy knowledge.