Paper Info
Title
ICFC: A method for computing semantic similarity among fuzzy concepts in a fuzzy ontology
Abstract
Semantic similarity calculation is an important step of ontology mapping that is an effective method to solve the problem of ontology heterogeneity. However, current semantic similarity methods can be only adapted to crisp concepts, that is to say, they are not sufficient for handling fuzzy concepts whose instances belong to them with memberships. Therefore, this paper proposes a novel semantic similarity method, ICFC (Information Content of Fuzzy Concept), for fuzzy concepts in a fuzzy ontology. In this method, a semantic similarity between two fuzzy concepts is computed by their information content values. But, it is difficult to obtain information content values for fuzzy concepts. That is because a fuzzy concept is considered as a fuzzy set, and all the elements (i.e., instances) belong to it with memberships. For this purpose, ICFC achieves two tasks: (i) determines whether or not an instance belongs to a fuzzy concept; (ii) provides a method for computing the membership of instance to fuzzy concept, which exploits the degrees that the attribute domains of fuzzy concept include the attribute values of instance. Experimental results with a fuzzy ontology from the real world indicate that ICFC performs encouragingly well.
Year
DOI
Venue
2012
10.1109/FUZZ-IEEE.2012.6251166
FUZZ-IEEE
Keywords
Field
DocType
fuzzy ontology,fuzzy set theory,information content of fuzzy concept,information content values,semantic similarity,information content,ontology mapping,icfc,semantic similarity calculation,ontology heterogeneity,semantic web,ontologies (artificial intelligence),novel semantic similarity method,vectors,semantics,ontologies,information science,probability
Data mining,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Fuzzy mathematics,Fuzzy set,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy number,Machine learning
Conference
Volume
Issue
ISSN
null
null
1098-7584 E-ISBN : 978-1-4673-1505-0
ISBN
Citations 
PageRank 
978-1-4673-1505-0
1
0.36
References 
Authors
16
2
Name
Order
Citations
PageRank
Lingyu Zhang131.07
Z. M. Ma260166.24