A fuzzy ontology modeling for case base knowledge in diabetes mellitus domain

被引：30

作者：

El-Sappagh, Shaker ^{[1
]}

Elmogy, Mohammed ^{[2
]}

机构：

[1] Menia Univ, Fac Comp & Informat, Al Minya, Egypt

[2] Mansoura Univ, Fac Comp & Informat, Mansoura, Egypt

来源：

ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH | 2017年 / 20卷 / 03期

关键词：

Case-based reasoning; Semantic retrieval; Case base representation; Fuzzy ontology; Diabetes diagnosis; SUPPORT; SYSTEM; CONSTRUCTION; FRAMEWORK; DESIGN;

D O I：

10.1016/j.jestch.2017.03.009

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Knowledge-Intensive Case-Based Reasoning Systems (KI-CBR) mainly depend on ontologies. Ontology can play the role of case-base knowledge. The combination of ontology and fuzzy logic reasoning is critical in the medical domain. Case-base representation based on fuzzy ontology is expected to enhance the semantic and storage of CBR knowledge-base. This paper provides an advancement to the research of diabetes diagnosis CBR by proposing a novel case-base fuzzy OWL2 ontology (CBRDiabOnto). This ontology can be considered as the first fuzzy case-base ontology in the medical domain. It is based on a case-base fuzzy Extended Entity Relation (EER) data model. It contains 63 (fuzzy) classes, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, and 105 fuzzy datatypes. We populated the ontology with 60 cases and used SPARQL-DL for its query. The evaluation of CBRDiabOnto shows that it is accurate, consistent, and cover terminologies and logic of diabetes mellitus diagnosis. (C) 2017 Karabuk University. Publishing services by Elsevier B.V.

引用

页码：1025 / 1040

页数：16

共 60 条

[1] Database workload management through CBR and fuzzy based characterization [J].