Reasoning within intuitionistic fuzzy rough description logics

Jiang, Yuncheng; Tang, Yong; Wang, Ju; Tang, Suqin

doi:10.1016/j.ins.2009.03.001

Cited by 52 publications

(19 citation statements)

References 60 publications

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“…Nowadays, properties and semantics of ontology constructs mainly are determined by Description Logics (DLs) [38][39][40], a family of logics for representing structured knowledge which have proved to be very useful as ontology languages [44]. Formally, an ontology is a triple O = ⟨RB, T B, AB⟩, where RB (the Role Box or RBox) and T B (the Terminological Box or TBox) comprise the intensional knowledge, i.e., general knowledge about the world to be described (statements about roles and concepts, respectively), and AB (the Assertional Box or ABox) the extensional knowledge, i.e., particular knowledge about a specific instantiation of this world (statements about individuals in terms of concepts and roles) [44].…”

Section: Ontologies and Description Logicsmentioning

confidence: 99%

“…In other words, each parameter is only a word or a sentence, and expressive (or complex) parameters are not considered in soft sets [37]. In order to extend the expressive power of soft sets, Jiang et al [37] used the concepts of Description Logics (DLs) [38][39][40] to act as the parameters of soft sets. That is, an extended soft set theory based on DLs was presented in [37].…”

Section: Introductionmentioning

confidence: 99%

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Semantic operations of multiple soft sets under conflict

Jiang

Tang

Chen

et al. 2011

Computers & Mathematics with Applications

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a b s t r a c tMolodtsov initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. Description Logics (DLs) are a family of knowledge representation languages which can be used to represent the terminological knowledge of an application domain in a structured and formally well-understood way. Nowadays, properties and semantics of ontology constructs mainly are determined by DLs. In this paper we investigate semantic operations of multiple standard soft sets by using domain ontologies (i.e., DL intensional knowledge bases). Concretely, we give some semantic operations such as complement, restricted difference, extended union, restricted intersection, restricted union, extended intersection, AND, and OR for (multiple) standard soft sets from a semantic point of view. Especially, we also present an approach to deal with conflict from a semantic point of view when we define these semantic operations. Moreover, the basic properties and implementation methods of these semantic operations under conflict are also presented and discussed.

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Section: Ontologies and Description Logicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic operations of multiple soft sets under conflict

Jiang

Tang

Chen

et al. 2011

Computers & Mathematics with Applications

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“…The sum of the membership degree and the non-membership degree of each ordered pair is less than or equal to one. Since it was first introduced in 1986, IFS theory has been widely investigated and applied to a variety of fields [2,4,12,13,20,22]. Information aggregation is an interesting and important research topic in IFS theory that has been receiving more and more attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Choquet integrals of weighted intuitionistic fuzzy information

Zhang

2010

Information Sciences

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“…Using the notion of a conventional fuzzy set, Atanassov [2] introduced the concept of an intuitionistic fuzzy set (IFS), which is a generalization of the fuzzy set, to provide additional information about indeterminacy degrees. Since IFSs can adequately measure the decision-making process of human beings and cope with incomplete information, the merits of IFSs have been applied in various fields, including logical reasoning [19], pattern recognition [16], and decision-making [36]. In particular, Li [22] and Lin et al [24] used a linear programming model to assess the optimal attribute weights in MADM.…”

Section: Introductionmentioning

confidence: 99%