Ontology representation and mapping of common fuzzy knowledge

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• 作者：Jie Liu^a ; ^b ; ^{liujie@cnu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Bo-Ju Zheng^aAuthor Vitae ; Li-Ming Luo^a ; ^bAuthor Vitae ; Jian-She Zhou^bAuthor Vitae ; Yuan Zhang^aAuthor Vitae ; Zheng-Tao Yu^cAuthor Vitae
• 关键词：Fuzzy knowledge representation ; Fuzzy ontology ; Ontology mapping ; Cloud model
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：26 November 2016
• 年：2016
• 卷：215
• 期：Complete
• 页码：184-195
• 全文大小：1438 K

文摘

Fuzzy knowledge is prevalent in real life, and ontology is the main way of knowledge representation in semantic web. In this paper, two main kinds of common fuzzy knowledge are sorted out, and there is a great significance for ontology representation of common fuzzy knowledge in semantic web. In addition, searching knowledge in ontology is the most common operation of semantic web, but heterogeneous ontologies seriously affect accuracy of information retrieval, and ontology mapping is the key to solve the problem. Therefore, in this paper, firstly an ontology representation method of common fuzzy knowledge is presented. Common fuzzy knowledge is polytypic and includes most commonly used fuzzy knowledge in reality. So fuzzy sets and Cloud Model are used to reflect and represent these 2 types of common fuzzy knowledge in ontology. Then, based on the ontology representation method of common fuzzy knowledge mentioned above, a corresponding algorithm of ontology mapping is presented, which is based on similarity calculation of concepts and Support Vector Machine. The proposed methods extend the scope of ontology application and are significant for information retrieval of fuzzy knowledge in the semantic web. The experiments show that the proposed methods are practicable and effective.