Download Fuzzy Knowledge Management for the Semantic Web (Studies in by Li Yan, Zongmin Ma, Fu Zhang, Jingwei Cheng PDF
By Li Yan, Zongmin Ma, Fu Zhang, Jingwei Cheng
This e-book is going to nice intensity in regards to the quick starting to be subject of applied sciences and techniques of fuzzy good judgment within the Semantic net. the themes of this booklet comprise fuzzy description logics and fuzzy ontologies, queries of fuzzy description logics and fuzzy ontology wisdom bases, extraction of fuzzy description logics and ontologies from fuzzy facts types, garage of fuzzy ontology wisdom bases in fuzzy databases, fuzzy Semantic net ontology mapping, and fuzzy principles and their interchange within the Semantic internet. The booklet goals to supply a unmarried list of present study within the fuzzy wisdom illustration and reasoning for the Semantic net. the target of the publication is to supply the state-of-the-art details to researchers, practitioners and graduate scholars of the net intelligence and even as serve the data and information engineering specialist confronted with non-traditional functions that make the appliance of traditional techniques tricky or most unlikely.
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This booklet is going to nice intensity about the quick becoming subject of applied sciences and ways of fuzzy good judgment within the Semantic internet. the themes of this booklet comprise fuzzy description logics and fuzzy ontologies, queries of fuzzy description logics and fuzzy ontology wisdom bases, extraction of fuzzy description logics and ontologies from fuzzy facts versions, garage of fuzzy ontology wisdom bases in fuzzy databases, fuzzy Semantic net ontology mapping, and fuzzy ideas and their interchange within the Semantic internet.
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Additional resources for Fuzzy Knowledge Management for the Semantic Web (Studies in Fuzziness and Soft Computing)
It is particular true in the knowledge representation and reasoning in the Semantic Web as well as applications using Semantic Web techniques such as ontologies, Description Logics, and rules. In particular, many of the existing approaches dealing with imprecision and uncertainty are based on the theory of fuzzy sets and possibility theory. Zadeh’s fuzzy set theory has been identified as a successful technique for modelling the fuzzy information in many application areas. For example, as we have known, fuzzy set theory has been extensively applied to extend various database models and resulted in numerous contributions as will be introduced in the next Chap.
6 shows a fuzzy dependency relationship, which is denoted by a dashed line with an arrowhead. It is clear that Employee Dependent is dependent on fuzzy class Employee with membership degree g [ [0, 1]. Fig. 5 Representation of a fuzzy aggregation in a fuzzy UML model New-Computer [1, ] Monitor [1, 1] New CPU box [1, ] Keyboard 42 Fig. 6 Representation of a fuzzy dependency in a fuzzy UML model Fig. 7 Representation of a fuzzy association (class) in a fuzzy UML model 3 Fuzzy Data Models and Formal Descriptions Employee Old-Employee WITH DEGREE Employee Dependent Uby Uof (1,1) (1,2) New-Computer Use Date: String : String Fuzzy Association An association is a relation between the instances of two or more classes.
In Fig. 3 means that each object instance of the class Old-Employee has at least one email, and possibly more. Fig. 3 Representation of a fuzzy class Old-Employee in a fuzzy UML model Old-Employee ID: String Name: String FUZZY Age: Integer FUZZY Email [1 ]: String FUZZY IsDepartment( ): String fuzzy class name crisp attributes fuzzy attributes method (operation) additional attribute u : Real 40 3 Fuzzy Data Models and Formal Descriptions • The method IsDepartment():String denotes the dynamic aspect of fuzzy UML models.